Education
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
These authors contributed equally to this work. Order was determined by rolling a dice.
Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep neural networks has focused on overcoming catastrophic forgetting of old tasks in gradient-based optimization.
Efficient Online Clustering with Moving Costs
The clients' positions may change in complex and unpredictable ways and thus an a priori knowledge
Preference-grounded Token-level Guidance for Language Model Fine-tuning Shentao Yang
Aligning language models (LMs) with preferences is an important problem in natural language generation.
4b0eea69deea512c9e2c469187643dc2-Paper-Conference.pdf
Ta s k: Your task is to boil tin.
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Recently, large language models (LLMs) ( e.g., GPT -3 and ChatGPT) have shown remarkable zero-shot and few-shot performance on various tasks [
SPRING: Studying the Paper and Reasoning to Play Games Yue Wu
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read Crafter's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM).
Evolving Standardization for Continual Domain Generalization over Temporal Drift
The capability of generalizing to out-of-distribution data is crucial for the deployment of machine learning models in the real world.