10 citations · 17 across the 7 of their papers we have counts for
5 papers · 1 filter
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer +6
Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR). In this paper, we aim to improve distillation methods that pave the…
When does mixup promote local linearity in learned representations?
Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit +3
Mixup is a regularization technique that artificially produces new samples using convex combinations of original training points. This simple technique has shown strong empirical p…
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum +4
Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all lab…
Kernelized Classification in Deep Networks
Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar
We propose a kernelized classification layer for deep networks. Although conventional deep networks introduce an abundance of nonlinearity for representation (feature) learning, th…
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat +3
Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples. This poses a chall…