Industry
Improving Robustness with Adaptive Weight Decay
Deep Neural Networks (DNNs) have exceeded human capability on many computer vision tasks.
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
The advent of large language models (LLMs) [Brown et al., 2020] has revolutionized the field of
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.
Core-sets for Fair and Diverse Data Summarization
Second, we show the first core-set w.r.t. the sum-of-nearest-neighbor distances. Finally, we run several experiments showing the effectiveness of our core-set approach. In particular, we apply constrained diversity maximization to summarize a set of timed messages that takes into account the messages' recency.
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
This prevailing paradigm heavily relies on the out-of-distribution (OOD) effect to influence the model's prediction.
Amortized Bayesian Experimental Design for Decision-Making
For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning Weikang Wan
This paper introduces DiffTORI, which utilizes Diff erentiable T rajectory O ptimization as the policy representation to generate actions for deep R einforcement and I mitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function.
