542 citations · 1.3k across the 17 of their papers we have counts for
21 papers
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
Joel Shor, Subhashini Venugopalan
In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one model may exploit model-specif…
From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer
Joel Shor
Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activity. Yet most model-based nu…
Predicting Generalization of AI Colonoscopy Models to Unseen Data
Joel Shor, Carson McNeil, Yotam Intrator +15
: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on…
The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries
Joel Shor, Hiro-o Yamano, Daisuke Tsurumaru +13
: Artificial Intelligence (AI) Computer-Aided Detection (CADe) is commonly used for polyp detection, but data seen in clinical settings can differ fro…
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
Joel Shor, Ruyue Agnes Bi, Subhashini Venugopalan +3
Automatic Speech Recognition (ASR) in medical contexts has the potential to save time, cut costs, increase report accuracy, and reduce physician burnout. However, the healthcare in…
Knowledge distillation for fast and accurate DNA sequence correction
Anastasiya Belyaeva, Joel Shor, Daniel E. Cook +11
Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease. The standard approach for generating DNA sequences from PacBio instruments rel…
The Need for Medically Aware Video Compression in Gastroenterology
Joel Shor, Nick Johnston
Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice re…
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Joel Shor, Subhashini Venugopalan
Recent advances in self-supervision have dramatically improved the quality of speech representations. However, deployment of state-of-the-art embedding models on devices has been r…
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Joel Shor, Aren Jansen, Wei Han +2
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishin…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal +4
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help…
FRILL: A Non-Semantic Speech Embedding for Mobile Devices
Jacob Peplinski, Joel Shor, Sachin Joglekar +2
Learned speech representations can drastically improve performance on tasks with limited labeled data. However, due to their size and complexity, learned representations have limit…
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor +7
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and l…
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Joel Shor, Dotan Emanuel, Oran Lang +9
Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from 'typical' speech, which means that underrepre…
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao +6
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing t…
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang +7
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The emb…
Spatially adaptive image compression using a tiled deep network
David Minnen, George Toderici, Michele Covell +6
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural n…
Uncovering Latent Style Factors for Expressive Speech Synthesis
Yuxuan Wang, RJ Skerry-Ryan, Ying Xiao +5
Prosodic modeling is a core problem in speech synthesis. The key challenge is producing desirable prosody from textual input containing only phonetic information. In this prelimina…
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Michele Covell, Nick Johnston, David Minnen +5
We introduce a stop-code tolerant (SCT) approach to training recurrent convolutional neural networks for lossy image compression. Our methods introduce a multi-pass training method…
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston, Damien Vincent, David Minnen +6
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We…
Full Resolution Image Compression with Recurrent Neural Networks
George Toderici, Damien Vincent, Nick Johnston +4
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates…