96 citations · 351 across the 28 of their papers we have counts for
12 papers · 1 filter
LLM Pretraining with Continuous Concepts
Jihoon Tack, Jack Lanchantin, Jane Yu +7
Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perpl…
A Data Source for Reasoning Embodied Agents
Jack Lanchantin, Sainbayar Sukhbaatar, Gabriel Synnaeve +3
Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datas…
Learning to Reason and Memorize with Self-Notes
Jack Lanchantin, Shubham Toshniwal, Jason Weston +2
Large language models have been shown to struggle with multi-step reasoning, and do not retain previous reasoning steps for future use. We propose a simple method for solving both…
Neural Message Passing for Multi-Label Classification
Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul chall…
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh +1
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TF…
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon +1
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation…
GaKCo: a Fast GApped k-mer string Kernel using COunting
Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari +3
String Kernel (SK) techniques, especially those using gapped -mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, t…
Memory Matching Networks for Genomic Sequence Classification
Jack Lanchantin, Ritambhara Singh, Yanjun Qi
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manual…
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Ritambhara Singh, Jack Lanchantin, Gabriel Robins +1
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related metho…
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Jack Lanchantin, Ritambhara Singh, Beilun Wang +1
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remai…
Deep Motif: Visualizing Genomic Sequence Classifications
Jack Lanchantin, Ritambhara Singh, Zeming Lin +1
This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task. To make the model understandable, we prop…
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Zeming Lin, Jack Lanchantin, Yanjun Qi
Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few dee…