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We believe in having open conversations for better scientific discourse. Here are some recent posts related to our work on social media (by us or a third party), where we find many of such discussions quite insightful. OpenReview offers an excellent place to digest papers from a non-author perspective; social media allows us to do exactly that for preprints. We'd try our best to engage in such posts. Of course, you are always welcome to email us or open up an issue anytime.
[r/MachineLearning, r/LocalLLaMA, Twitter/X, LinkedIn] KV Cache is huge and bottlenecks LLM inference. We quantize them to 2bit in a finetuning-free + plug-and-play fashion. authors
[r/LocalLLaMA, Twitter/X #1 #2 #3 #4, kexue.fm] LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning. third-party
Some recent work from us that might worth your attention.
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [paper]llm efficiency
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt [paper] llm efficiency
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning [paper]llm long context
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length [paper] llm long context
LETA: Learning Transferable Attribution for Generic Vision Explainer [paper]vision xai
Large Language Models As Faithful Explainers [paper] llm xai
On the Equivalence of Graph Convolution and Mixup [paper] graph
Chasing Fairness in Graphs: A GNN Architecture Perspective [paper] graph trustworthy
Editable Graph Neural Network for Node Classifications [paper] graph trustworthy
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [paper] llm survey
Data-centric Artificial Intelligence: A Survey [paper] dcai survey
The Science of Detecting LLM-Generated Texts llm security
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods graph benchmark trustworthy
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model llm efficiency
One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning efficiency trustworthy security
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots llm security
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach trustworthy
Fair Graph Distillation graph trustworthy
Double wins: Boosting accuracy and efficiency of graph neural networks by reliable knowledge distillation graph efficiency
LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability healthcare llm
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant healthcare
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research healthcare
Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks security
Data-centric AI: Perspectives and Challenges survey dcai
Context-aware Domain Adaptation for Time Series Anomaly Detection time series
Adaptive Label Smoothing To Regularize Large-Scale Graph Training graph
DIVISION: Memory Efficient Training via Dual Activation Precision vision efficiency
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations graph efficiency
PME: pruning-based multi-size embedding for recommender systems recsys efficiency
Pre-trained Neural Cost Models for Efficient Embedding Table Sharding in Deep Learning Recommendation Models recsys efficiency
CoRTX: Contrastive Framework for Real-time Explanation xai
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization graph efficiency