paperoftheday.json

[{"title":"Auto-Lambda: Disentangling Dynamic Task Relationships","link":"https://paperswithcode.com/paper/auto-lambda-disentangling-dynamic-task","description":"Unlike previous methods where task relationships are assumed to be fixed, Auto-Lambda is a gradient-based meta learning framework which explores continuous, dynamic task relationships via task-specific weightings, and can optimise any choice of combination of tasks through the formulation of a meta-loss; where the validation loss automatically influences task weightings throughout training.","date":"2/9/2022"},{"title":"PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX","link":"https://paperswithcode.com/paper/pgmax-factor-graphs-for-discrete","description":"PGMax is an open-source Python package for easy specification of discrete Probabilistic Graphical Models (PGMs) as factor graphs, and automatic derivation of efficient and scalable loopy belief propagation (LBP) implementation in JAX.","date":"2/10/2022"},{"title":"Semi-Supervised Convolutive NMF for Automatic Music Transcription","link":"https://paperswithcode.com/paper/semi-supervised-convolutive-nmf-for-automatic","description":"Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic foßrmat, remains a difficult Music Information Retrieval task.","date":"2/11/2022"},{"title":"Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging","link":"https://paperswithcode.com/paper/image-to-image-regression-with-distribution","description":"Image-to-image regression is an important learning task, used frequently in biological imaging.","date":"2/12/2022"},{"title":"The leap to ordinal: functional prognosis after traumatic brain injury using artificial intelligence","link":"https://paperswithcode.com/paper/the-leap-to-ordinal-functional-prognosis","description":"We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning.","date":"2/13/2022"},{"title":"SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics","link":"https://paperswithcode.com/paper/supa-a-lightweight-diagnostic-simulator-for","description":"Our contribution is SUPA, the SUrrogate PArticle propagation simulator, an algorithm and software package for generating data by simulating simplified particle propagation, scattering and shower development in matter.","date":"2/14/2022"},{"title":"Source Code Summarization with Structural Relative Position Guided Transformer","link":"https://paperswithcode.com/paper/source-code-summarization-with-structural","description":"We further show that how the proposed SCRIPT captures the structural relative dependencies.","date":"2/15/2022"},{"title":"A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images","link":"https://paperswithcode.com/paper/a-pragmatic-machine-learning-approach-to","description":"Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data.","date":"2/16/2022"},{"title":"One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization","link":"https://paperswithcode.com/paper/one-configuration-to-rule-them-all-towards","description":"Topic models are statistical methods that extract underlying topics from document collections.","date":"2/17/2022"},{"title":"cosFormer: Rethinking Softmax in Attention","link":"https://paperswithcode.com/paper/cosformer-rethinking-softmax-in-attention-1","description":"As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length.","date":"2/18/2022"},{"title":"A study of deep perceptual metrics for image quality assessment","link":"https://paperswithcode.com/paper/a-study-of-deep-perceptual-metrics-for-image","description":"Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images.","date":"2/19/2022"},{"title":"Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth","link":"https://paperswithcode.com/paper/hamilton-jacobi-equations-on-graphs-with","description":"We show that the $p$-eikonal equation with $p=1$ is a provably robust distance-type function on a graph, and the $p\\to \\infty$ limit recovers shortest path distances.","date":"2/20/2022"},{"title":"TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting","link":"https://paperswithcode.com/paper/tafnet-a-three-stream-adaptive-fusion-network","description":"Specifically, TAFNet is divided into one main stream and two auxiliary streams.","date":"2/21/2022"},{"title":"Same Cause; Different Effects in the Brain","link":"https://paperswithcode.com/paper/same-cause-different-effects-in-the-brain","description":"It is then natural to ask: \"Is the activity in these different brain zones caused by the stimulus properties in the same way?\"","date":"2/22/2022"},{"title":"Vision-Language Pre-Training with Triple Contrastive Learning","link":"https://paperswithcode.com/paper/vision-language-pre-training-with-triple","description":"Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning.","date":"2/23/2022"},{"title":"Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference","link":"https://paperswithcode.com/paper/embarrassingly-simple-performance-prediction","description":"We do this by testing how well the pre-trained models perform on the \\alpha{}nli task when just comparing sentence embeddings with cosine similarity to what the performance that is achieved when training a classifier on top of these embeddings.","date":"2/24/2022"},{"title":"Transferring Adversarial Robustness Through Robust Representation Matching","link":"https://paperswithcode.com/paper/transferring-adversarial-robustness-through","description":"On CIFAR-10, RRM trains a robust model $\\sim 1. 8\\times$ faster than the state-of-the-art.","date":"2/25/2022"},{"title":"Variational encoding approach for interpretable assessment of remaining useful life estimation","link":"https://paperswithcode.com/paper/variational-encoding-approach-for","description":"However, most of them lack an explanatory component to understand model learning and/or the nature of the data.","date":"2/26/2022"},{"title":"BERTVision -- A Parameter-Efficient Approach for Question Answering","link":"https://paperswithcode.com/paper/bertvision-a-parameter-efficient-approach-for","description":"We present a highly parameter efficient approach for Question Answering that significantly reduces the need for extended BERT fine-tuning.","date":"2/27/2022"},{"title":"Sky Computing: Accelerating Geo-distributed Computing in Federated Learning","link":"https://paperswithcode.com/paper/sky-computing-accelerating-geo-distributed","description":"In this paper, we proposed Sky Computing, a load-balanced model parallelism framework to adaptively allocate the weights to devices.","date":"2/28/2022"},{"title":"Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis","link":"https://paperswithcode.com/paper/precision-medicine-toolbox-an-open-source","description":"Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment.","date":"3/1/2022"},{"title":"Differential equation and probability inspired graph neural networks for latent variable learning","link":"https://paperswithcode.com/paper/differential-equation-and-probability","description":"Inspired by probabilistic theory and differential equations, this paper proposes graph neural networks to solve state estimation and subspace learning problems.","date":"3/2/2022"},{"title":"Differential equation and probability inspired graph neural networks for latent variable learning","link":"https://paperswithcode.com/paper/differential-equation-and-probability","description":"Inspired by probabilistic theory and differential equations, this paper proposes graph neural networks to solve state estimation and subspace learning problems.","date":"3/3/2022"},{"title":"Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking","link":"https://paperswithcode.com/paper/dialogue-summaries-as-dialogue-states-ds2","description":"In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.","date":"3/4/2022"},{"title":"Constrained unsupervised anomaly segmentation","link":"https://paperswithcode.com/paper/constrained-unsupervised-anomaly-segmentation","description":"In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.","date":"3/5/2022"},{"title":"An Open Challenge for Inductive Link Prediction on Knowledge Graphs","link":"https://paperswithcode.com/paper/an-open-challenge-for-inductive-link","description":"An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.","date":"3/6/2022"},{"title":"Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking","link":"https://paperswithcode.com/paper/dialogue-summaries-as-dialogue-states-ds2","description":"In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.","date":"3/7/2022"},{"title":"SoftGroup for 3D Instance Segmentation on Point Clouds","link":"https://paperswithcode.com/paper/softgroup-for-3d-instance-segmentation-on","description":"The hard predictions are made when performing semantic segmentation such that each point is associated with a single class.","date":"3/8/2022"},{"title":"LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network","link":"https://paperswithcode.com/paper/lgt-net-indoor-panoramic-room-layout","description":"We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions.","date":"3/9/2022"},{"title":"PetsGAN: Rethinking Priors for Single Image Generation","link":"https://paperswithcode.com/paper/petsgan-rethinking-priors-for-single-image","description":"Moreover, we apply our method to other image manipulation tasks (e. g., style transfer, harmonization), and the results further prove the effectiveness and efficiency of our method.","date":"3/10/2022"},{"title":"A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation","link":"https://paperswithcode.com/paper/a-simple-hash-based-early-exiting-approach-1","description":"Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning.","date":"3/11/2022"},{"title":"Selective Residual M-Net for Real Image Denoising","link":"https://paperswithcode.com/paper/selective-residual-m-net-for-real-image","description":"Image restoration is a low-level vision task which is to restore degraded images to noise-free images.","date":"3/12/2022"},{"title":"A low-rank ensemble Kalman filter for elliptic observations","link":"https://paperswithcode.com/paper/a-low-rank-ensemble-kalman-filter-for","description":"We propose a regularization method for ensemble Kalman filtering (EnKF) with elliptic observation operators.","date":"3/13/2022"},{"title":"Multi-modal Graph Learning for Disease Prediction","link":"https://paperswithcode.com/paper/multi-modal-graph-learning-for-disease-1","description":"For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e. g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL).","date":"3/14/2022"},{"title":"SoftGroup for 3D Instance Segmentation on Point Clouds","link":"https://paperswithcode.com/paper/softgroup-for-3d-instance-segmentation-on","description":"The hard predictions are made when performing semantic segmentation such that each point is associated with a single class.","date":"3/15/2022"},{"title":"Constrained unsupervised anomaly segmentation","link":"https://paperswithcode.com/paper/constrained-unsupervised-anomaly-segmentation","description":"In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.","date":"3/16/2022"},{"title":"An Open Challenge for Inductive Link Prediction on Knowledge Graphs","link":"https://paperswithcode.com/paper/an-open-challenge-for-inductive-link","description":"An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.","date":"3/17/2022"},{"title":"PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization","link":"https://paperswithcode.com/paper/peersum-a-peer-review-dataset-for-abstractive-1","description":"We present PeerSum, a new MDS dataset using peer reviews of scientific publications.","date":"3/18/2022"},{"title":"A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation","link":"https://paperswithcode.com/paper/a-simple-hash-based-early-exiting-approach-1","description":"Previous works usually adopt heuristic metrics such as the entropy of internal outputs to measure instance difficulty, which suffers from generalization and threshold-tuning.","date":"3/19/2022"},{"title":"An Open Challenge for Inductive Link Prediction on Knowledge Graphs","link":"https://paperswithcode.com/paper/an-open-challenge-for-inductive-link","description":"An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.","date":"3/20/2022"},{"title":"Selective Residual M-Net for Real Image Denoising","link":"https://paperswithcode.com/paper/selective-residual-m-net-for-real-image","description":"Image restoration is a low-level vision task which is to restore degraded images to noise-free images.","date":"3/21/2022"},{"title":"LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network","link":"https://paperswithcode.com/paper/lgt-net-indoor-panoramic-room-layout","description":"We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions.","date":"3/22/2022"},{"title":"Generating natural images with direct Patch Distributions Matching","link":"https://paperswithcode.com/paper/generating-natural-images-with-direct-patch","description":"On a number of image generation tasks we show that our results are often superior to single-image-GANs, require no training, and can generate high quality images in a few seconds.","date":"3/23/2022"},{"title":"Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos","link":"https://paperswithcode.com/paper/look-for-the-change-learning-object-states","description":"In this paper, we seek to temporally localize object states (e. g. \"empty\" and \"full\" cup) together with the corresponding state-modifying actions (\"pouring coffee\") in long uncurated videos with minimal supervision.","date":"3/24/2022"},{"title":"Scale-out Systolic Arrays","link":"https://paperswithcode.com/paper/scale-out-systolic-arrays","description":"In this work, we study three key pillars in multi-pod systolic array designs, namely array granularity, interconnect, and tiling.","date":"3/25/2022"},{"title":"Scale-out Systolic Arrays","link":"https://paperswithcode.com/paper/scale-out-systolic-arrays","description":"In this work, we study three key pillars in multi-pod systolic array designs, namely array granularity, interconnect, and tiling.","date":"3/26/2022"},{"title":"Associating Objects with Scalable Transformers for Video Object Segmentation","link":"https://paperswithcode.com/paper/associating-objects-with-scalable","description":"The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computation resources.","date":"3/27/2022"},{"title":"Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals","link":"https://paperswithcode.com/paper/modelling-continual-learning-in-humans-with","description":"Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting.","date":"3/28/2022"},{"title":"Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos","link":"https://paperswithcode.com/paper/look-for-the-change-learning-object-states","description":"In this paper, we seek to temporally localize object states (e. g. \"empty\" and \"full\" cup) together with the corresponding state-modifying actions (\"pouring coffee\") in long uncurated videos with minimal supervision.","date":"3/29/2022"},{"title":"SurvCaus : Representation Balancing for Survival Causal Inference","link":"https://paperswithcode.com/paper/survcaus-representation-balancing-for","description":"Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.","date":"3/30/2022"},{"title":"Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection","link":"https://paperswithcode.com/paper/frequency-dynamic-convolution-frequency","description":"In addition, by comparing class-wise F1 scores of baseline model and frequency dynamic convolution, we showed that frequency dynamic convolution is especially more effective for detection of non-stationary sound events.","date":"3/31/2022"},{"title":"A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization","link":"https://paperswithcode.com/paper/a-two-phase-framework-with-a-bezier-simplex","description":"The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.","date":"4/1/2022"},{"title":"Selective inference for k-means clustering","link":"https://paperswithcode.com/paper/selective-inference-for-k-means-clustering","description":"We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering.","date":"4/2/2022"},{"title":"LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT","link":"https://paperswithcode.com/paper/lighthubert-lightweight-and-configurable","description":"LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.","date":"4/3/2022"},{"title":"Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection","link":"https://paperswithcode.com/paper/frequency-dynamic-convolution-frequency","description":"In addition, by comparing class-wise F1 scores of baseline model and frequency dynamic convolution, we showed that frequency dynamic convolution is especially more effective for detection of non-stationary sound events.","date":"4/4/2022"},{"title":"SurvCaus : Representation Balancing for Survival Causal Inference","link":"https://paperswithcode.com/paper/survcaus-representation-balancing-for","description":"Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.","date":"4/5/2022"},{"title":"LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT","link":"https://paperswithcode.com/paper/lighthubert-lightweight-and-configurable","description":"LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.","date":"4/6/2022"},{"title":"Category Guided Attention Network for Brain Tumor Segmentation in MRI","link":"https://paperswithcode.com/paper/category-guided-attention-network-for-brain","description":"In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.","date":"4/8/2022"},{"title":"StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis","link":"https://paperswithcode.com/paper/stylet2i-toward-compositional-and-high","description":"Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.","date":"4/9/2022"},{"title":"Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification","link":"https://paperswithcode.com/paper/alignment-uniformity-aware-representation","description":"Further, we synthesize features of unseen classes by proposing a class generator that interpolates and extrapolates the features of seen classes.","date":"4/11/2022"},{"title":"LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT","link":"https://paperswithcode.com/paper/lighthubert-lightweight-and-configurable","description":"LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.","date":"4/13/2022"},{"title":"SurvCaus : Representation Balancing for Survival Causal Inference","link":"https://paperswithcode.com/paper/survcaus-representation-balancing-for","description":"Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.","date":"4/14/2022"},{"title":"A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization","link":"https://paperswithcode.com/paper/a-two-phase-framework-with-a-bezier-simplex","description":"The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.","date":"4/15/2022"},{"title":"A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization","link":"https://paperswithcode.com/paper/a-two-phase-framework-with-a-bezier-simplex","description":"The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.","date":"4/16/2022"},{"title":"A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization","link":"https://paperswithcode.com/paper/a-two-phase-framework-with-a-bezier-simplex","description":"The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.","date":"4/17/2022"},{"title":"StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis","link":"https://paperswithcode.com/paper/stylet2i-toward-compositional-and-high","description":"Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.","date":"4/18/2022"},{"title":"SurvCaus : Representation Balancing for Survival Causal Inference","link":"https://paperswithcode.com/paper/survcaus-representation-balancing-for","description":"Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.","date":"4/19/2022"},{"title":"LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT","link":"https://paperswithcode.com/paper/lighthubert-lightweight-and-configurable","description":"LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.","date":"4/20/2022"},{"title":"Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification","link":"https://paperswithcode.com/paper/alignment-uniformity-aware-representation","description":"Further, we synthesize features of unseen classes by proposing a class generator that interpolates and extrapolates the features of seen classes.","date":"4/21/2022"},{"title":"Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection","link":"https://paperswithcode.com/paper/frequency-dynamic-convolution-frequency","description":"In addition, by comparing class-wise F1 scores of baseline model and frequency dynamic convolution, we showed that frequency dynamic convolution is especially more effective for detection of non-stationary sound events.","date":"4/23/2022"},{"title":"StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis","link":"https://paperswithcode.com/paper/stylet2i-toward-compositional-and-high","description":"Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.","date":"4/24/2022"},{"title":"LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT","link":"https://paperswithcode.com/paper/lighthubert-lightweight-and-configurable","description":"LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.","date":"4/25/2022"},{"title":"Analysis of EEG frequency bands for Envisioned Speech Recognition","link":"https://paperswithcode.com/paper/analysis-of-eeg-frequency-bands-for","description":"However, there has been limited work in identifying the frequency bands ($\\delta, \\theta, \\alpha, \\beta, \\gamma$) of the EEG signal that contribute towards envisioned speech recognition.","date":"4/27/2022"},{"title":"StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis","link":"https://paperswithcode.com/paper/stylet2i-toward-compositional-and-high","description":"Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.","date":"4/28/2022"},{"title":"SurvCaus : Representation Balancing for Survival Causal Inference","link":"https://paperswithcode.com/paper/survcaus-representation-balancing-for","description":"Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.","date":"4/29/2022"},{"title":"Two New Datasets for Italian-Language Abstractive Text Summarization","link":"https://paperswithcode.com/paper/two-new-datasets-for-italian-language","description":"Text summarization aims to produce a short summary containing relevant parts from a given text.","date":"4/30/2022"},{"title":"Two New Datasets for Italian-Language Abstractive Text Summarization","link":"https://paperswithcode.com/paper/two-new-datasets-for-italian-language","description":"Text summarization aims to produce a short summary containing relevant parts from a given text.","date":"5/2/2022"},{"title":"TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models","link":"https://paperswithcode.com/paper/temporalwiki-a-lifelong-benchmark-for-1","description":"Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.","date":"5/3/2022"},{"title":"C3-STISR: Scene Text Image Super-resolution with Triple Clues","link":"https://paperswithcode.com/paper/c3-stisr-scene-text-image-super-resolution","description":"In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.","date":"5/5/2022"},{"title":"OSSGAN: Open-Set Semi-Supervised Image Generation","link":"https://paperswithcode.com/paper/ossgan-open-set-semi-supervised-image","description":"We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.","date":"5/6/2022"},{"title":"CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification","link":"https://paperswithcode.com/paper/clip-art-contrastive-pre-training-for-fine-1","description":"Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.","date":"5/9/2022"},{"title":"Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach","link":"https://paperswithcode.com/paper/cost-effective-mlaas-federation-a","description":"With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.","date":"5/12/2022"},{"title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","link":"https://paperswithcode.com/paper/detecting-textual-adversarial-examples-based","description":"Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.","date":"5/13/2022"},{"title":"SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization","link":"https://paperswithcode.com/paper/scs-co-self-consistent-style-contrastive","description":"In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.","date":"5/15/2022"},{"title":"TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models","link":"https://paperswithcode.com/paper/temporalwiki-a-lifelong-benchmark-for-1","description":"Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.","date":"5/17/2022"},{"title":"OSSGAN: Open-Set Semi-Supervised Image Generation","link":"https://paperswithcode.com/paper/ossgan-open-set-semi-supervised-image","description":"We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.","date":"5/18/2022"},{"title":"Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach","link":"https://paperswithcode.com/paper/cost-effective-mlaas-federation-a","description":"With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.","date":"5/19/2022"},{"title":"A Challenging Benchmark of Anime Style Recognition","link":"https://paperswithcode.com/paper/a-challenging-benchmark-of-anime-style","description":"Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem.","date":"5/21/2022"},{"title":"OSSGAN: Open-Set Semi-Supervised Image Generation","link":"https://paperswithcode.com/paper/ossgan-open-set-semi-supervised-image","description":"We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.","date":"5/23/2022"},{"title":"OSSGAN: Open-Set Semi-Supervised Image Generation","link":"https://paperswithcode.com/paper/ossgan-open-set-semi-supervised-image","description":"We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.","date":"5/24/2022"},{"title":"OSSGAN: Open-Set Semi-Supervised Image Generation","link":"https://paperswithcode.com/paper/ossgan-open-set-semi-supervised-image","description":"We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.","date":"5/26/2022"},{"title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","link":"https://paperswithcode.com/paper/detecting-textual-adversarial-examples-based","description":"Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.","date":"5/27/2022"},{"title":"SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization","link":"https://paperswithcode.com/paper/scs-co-self-consistent-style-contrastive","description":"In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.","date":"5/28/2022"},{"title":"A Challenging Benchmark of Anime Style Recognition","link":"https://paperswithcode.com/paper/a-challenging-benchmark-of-anime-style","description":"Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem.","date":"5/29/2022"},{"title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","link":"https://paperswithcode.com/paper/detecting-textual-adversarial-examples-based","description":"Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.","date":"5/30/2022"},{"title":"SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization","link":"https://paperswithcode.com/paper/scs-co-self-consistent-style-contrastive","description":"In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.","date":"5/31/2022"},{"title":"Deep Geometry Post-Processing for Decompressed Point Clouds","link":"https://paperswithcode.com/paper/deep-geometry-post-processing-for","description":"Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.","date":"6/1/2022"},{"title":"Deep Geometry Post-Processing for Decompressed Point Clouds","link":"https://paperswithcode.com/paper/deep-geometry-post-processing-for","description":"Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.","date":"6/2/2022"},{"title":"Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach","link":"https://paperswithcode.com/paper/cost-effective-mlaas-federation-a","description":"With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.","date":"6/3/2022"},{"title":"Deep Geometry Post-Processing for Decompressed Point Clouds","link":"https://paperswithcode.com/paper/deep-geometry-post-processing-for","description":"Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.","date":"6/4/2022"},{"title":"VPNets: Volume-preserving neural networks for learning source-free dynamics","link":"https://paperswithcode.com/paper/vpnets-volume-preserving-neural-networks-for","description":"We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data.","date":"6/5/2022"},{"title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","link":"https://paperswithcode.com/paper/detecting-textual-adversarial-examples-based","description":"Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.","date":"6/6/2022"},{"title":"TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models","link":"https://paperswithcode.com/paper/temporalwiki-a-lifelong-benchmark-for-1","description":"Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.","date":"6/9/2022"},{"title":"VPNets: Volume-preserving neural networks for learning source-free dynamics","link":"https://paperswithcode.com/paper/vpnets-volume-preserving-neural-networks-for","description":"We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data.","date":"6/10/2022"},{"title":"Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach","link":"https://paperswithcode.com/paper/cost-effective-mlaas-federation-a","description":"With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.","date":"6/11/2022"},{"title":"A Challenging Benchmark of Anime Style Recognition","link":"https://paperswithcode.com/paper/a-challenging-benchmark-of-anime-style","description":"Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem.","date":"6/12/2022"},{"title":"SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization","link":"https://paperswithcode.com/paper/scs-co-self-consistent-style-contrastive","description":"In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.","date":"6/13/2022"},{"title":"Deep Geometry Post-Processing for Decompressed Point Clouds","link":"https://paperswithcode.com/paper/deep-geometry-post-processing-for","description":"Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.","date":"6/14/2022"},{"title":"TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models","link":"https://paperswithcode.com/paper/temporalwiki-a-lifelong-benchmark-for-1","description":"Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.","date":"6/15/2022"},{"title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","link":"https://paperswithcode.com/paper/detecting-textual-adversarial-examples-based","description":"Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly classified inputs, successfully fool highly expressive deep classifiers into incorrect predictions.","date":"6/16/2022"},{"title":"CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification","link":"https://paperswithcode.com/paper/clip-art-contrastive-pre-training-for-fine-1","description":"Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.","date":"6/17/2022"},{"title":"SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization","link":"https://paperswithcode.com/paper/scs-co-self-consistent-style-contrastive","description":"In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.","date":"6/18/2022"},{"title":"CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification","link":"https://paperswithcode.com/paper/clip-art-contrastive-pre-training-for-fine-1","description":"Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.","date":"6/20/2022"},{"title":"A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks","link":"https://paperswithcode.com/paper/a-unified-evaluation-of-textual-backdoor","description":"However, we highlight two issues in previous backdoor learning evaluations: (1) The differences between real-world scenarios (e. g. releasing poisoned datasets or models) are neglected, and we argue that each scenario has its own constraints and concerns, thus requires specific evaluation protocols; (2) The evaluation metrics only consider whether the attacks could flip the models' predictions on poisoned samples and retain performances on benign samples, but ignore that poisoned samples should also be stealthy and semantic-preserving.","date":"6/21/2022"},{"title":"Plotly-Resampler: Effective Visual Analytics for Large Time Series","link":"https://paperswithcode.com/paper/plotly-resampler-effective-visual-analytics","description":"We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization.","date":"6/25/2022"},{"title":"SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving","link":"https://paperswithcode.com/paper/saferl-kit-evaluating-efficient-reinforcement","description":"Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well.","date":"6/27/2022"}]