Deep Learning
Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models
Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of superficial variations in the operands.
EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography Jehan Y ang
By predicting the user's intended
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Among these methods, data pruning incurs the least additional training cost and offers the most practical acceleration benefits.
Towards Multi-Domain Learning for Generalizable Video Anomaly Detection
Work done while doing an internship at NA VER Cloud.
Learning Successor Features the Simple Way Raymond Chua
In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments.
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
In this paper, we propose a Hybrid Multimodal Memory module to address the above challenges.