Search Results for author: Aadyot Bhatnagar

Found 8 papers, 3 papers with code

Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion

1 code implementation21 Dec 2023 Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani

In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise.

Improved Online Conformal Prediction via Strongly Adaptive Online Learning

2 code implementations15 Feb 2023 Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai

We prove that our methods achieve near-optimal strongly adaptive regret for all interval lengths simultaneously, and approximately valid coverage.

Conformal Prediction Image Classification +4

Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE

no code implementations19 Oct 2021 Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong

Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE matches a pre-specified prior distribution.

Contrastive Learning Representation Learning

Local Calibration: Metrics and Recalibration

no code implementations22 Feb 2021 Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone

In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.

Decision Making Fairness

Momentum Contrastive Autoencoder

no code implementations1 Jan 2021 Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong

Quantitatively, we show that our algorithm achieves a new state-of-the-art FID of 54. 36 on CIFAR-10, and performs competitively with existing models on CelebA in terms of FID score.

Contrastive Learning Representation Learning

An investigation of phone-based subword units for end-to-end speech recognition

no code implementations8 Apr 2020 Weiran Wang, Guangsen Wang, Aadyot Bhatnagar, Yingbo Zhou, Caiming Xiong, Richard Socher

For Switchboard, our phone-based BPE system achieves 6. 8\%/14. 4\% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6. 3\%/13. 3\% WER.

Language Modelling speech-recognition +1

Learning to Search via Retrospective Imitation

no code implementations3 Apr 2018 Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono

We study the problem of learning a good search policy for combinatorial search spaces.

Imitation Learning

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