Deep Learning
Linear Transformers are Versatile In-Context Learners
The transformer architecture (V aswani et al., 2017) has revolutionized the field of machine learning,
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate Fan-Ming Luo 1,2 Zuolin Tu
Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks.
56ad264ac7448239145606cf4106042f-Paper-Conference.pdf
Anomaly detection in time series data is fundamental to the design, deployment, and evaluation of industrial control systems.
Learning Better Representations From Less Data For Propositional Satisfiability
Boolean satisfiability (SA T) is a fundamental problem in computer science.
Evaluating Vision-Language Models in the Wild with Human Preferences
Our comprehensive analysis of 20K real-world interactions reveals important insights into the failure cases of top-performing VLMs.
HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction
Citation networks are critical infrastructures of modern science, serving as intricate webs of past literature and enabling researchers to navigate the knowledge production system. To mine information hiding in the link space of such networks, predicting which previous papers (candidates) will a new paper (query) cite is a critical problem that has long been studied. However, an important gap remains unaddressed: the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts.