1.3k citations · 12.3k across the 104 of their papers we have counts for
32 papers · 1 filter
Learning from Noisy Anchors for One-stage Object Detection
Hengduo Li, Zuxuan Wu, Chen Zhu +3
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their inte…
Predicting with High Correlation Features
Devansh Arpit, Caiming Xiong, Richard Socher
It has been shown that instead of learning actual object features, deep networks tend to exploit non-robust (spurious) discriminative features that are shared between training and…
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong +1
While using shaped rewards can be beneficial when solving sparse reward tasks, their successful application often requires careful engineering and is problem specific. For instance…
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi +2
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationshi…
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models
Linqing Liu, Huan Wang, Jimmy Lin +2
Pretrained language models have led to significant performance gains in many NLP tasks. However, the intensive computing resources to train such models remain an issue. Knowledge d…
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani +4
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased inte…