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09/04/2023
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
The prediction of molecular properties is a crucial task in the field of...
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05/31/2023
The Impact of Positional Encoding on Length Generalization in Transformers
Length generalization, the ability to generalize from small training con...
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05/22/2023
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models
Auditing unwanted social bias in language models (LMs) is inherently har...
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05/17/2023
Equivariant Few-Shot Learning from Pretrained Models
Efficient transfer learning algorithms are key to the success of foundat...
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03/11/2023
Enhancing Protein Language Models with Structure-based Encoder and Pre-training
Protein language models (PLMs) pre-trained on large-scale protein sequen...
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01/28/2023
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction
Pre-training methods on proteins are recently gaining interest, leveragi...
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01/08/2023
AI Maintenance: A Robustness Perspective
With the advancements in machine learning (ML) methods and compute resou...
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01/05/2023
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning
Machine Learning-guided solutions for protein learning tasks have made s...
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11/18/2022
Knowledge Graph Generation From Text
In this work we propose a novel end-to-end multi-stage Knowledge Graph (...
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11/08/2022
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
The demonstrated success of transfer learning has popularized approaches...
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11/01/2022
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Generative Flow Networks (GFlowNets) have demonstrated significant perfo...
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10/13/2022
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
We introduce equi-tuning, a novel fine-tuning method that transforms (po...
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10/06/2022
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Recent success in fine-tuning large models, that are pretrained on broad...
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10/05/2022
AlphaFold Distillation for Improved Inverse Protein Folding
Inverse protein folding, i.e., designing sequences that fold into a give...
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08/13/2022
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
With the prospect of automating a number of chemical tasks with high fid...
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08/10/2022
Active Sampling of Multiple Sources for Sequential Estimation
Consider K processes, each generating a sequence of identical and indepe...
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07/14/2022
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data
Training generative models that capture rich semantics of the data and i...
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07/08/2022
GT4SD: Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains,...
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05/20/2022
Learning Geometrically Disentangled Representations of Protein Folding Simulations
Massive molecular simulations of drug-target proteins have been used as ...
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04/19/2022
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework
The COVID-19 pandemic has highlighted the urgency for developing more ef...
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03/15/2022
Data-Efficient Graph Grammar Learning for Molecular Generation
The problem of molecular generation has received significant attention r...
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03/11/2022
Protein Representation Learning by Geometric Structure Pretraining
Learning effective protein representations is critical in a variety of t...
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02/08/2022
Fourier Representations for Black-Box Optimization over Categorical Variables
Optimization of real-world black-box functions defined over purely categ...
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12/02/2021
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
Photo-acid generators (PAGs) are compounds that release acids (H^+ ions)...
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11/14/2021
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
This paper investigates the problem of best arm identification in contam...
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11/12/2021
Benchmarking deep generative models for diverse antibody sequence design
Computational protein design, i.e. inferring novel and diverse protein s...
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11/10/2021
Physics-enhanced deep surrogates for PDEs
We present a "physics-enhanced deep-surrogate ("PEDS") approach towards ...
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08/27/2021
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Automatic construction of relevant Knowledge Bases (KBs) from text, and ...
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08/18/2021
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention
Current methods for viral discovery target evolutionarily conserved prot...
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06/24/2021
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a...
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06/17/2021
Do Large Scale Molecular Language Representations Capture Important Structural Information?
Predicting chemical properties from the structure of a molecule is of gr...
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06/09/2021
Predicting Deep Neural Network Generalization with Perturbation Response Curves
The field of Deep Learning is rich with empirical evidence of human-like...
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06/08/2021
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Deep generative models have emerged as a powerful tool for learning info...
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04/08/2021
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like...
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04/01/2021
Towards creativity characterization of generative models via group-based subset scanning
Deep generative models, such as Variational Autoencoders (VAEs), have be...
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12/22/2020
Self-Progressing Robust Training
Enhancing model robustness under new and even adversarial environments i...
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12/07/2020
Reprogramming Language Models for Molecular Representation Learning
Recent advancements in transfer learning have made it a promising approa...
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11/03/2020
Optimizing Molecules using Efficient Queries from Property Evaluations
Machine learning has shown potential for optimizing existing molecules w...
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10/27/2020
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
In this work, we present a dual learning approach for unsupervised text ...
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10/18/2020
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
Deep generative models are increasingly becoming integral parts of the i...
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09/23/2020
Explaining Chemical Toxicity using Missing Features
Chemical toxicity prediction using machine learning is important in drug...
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09/05/2020
Optimizing Mode Connectivity via Neuron Alignment
The loss landscapes of deep neural networks are not well understood due ...
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08/24/2020
Active learning of deep surrogates for PDEs: Application to metasurface design
Surrogate models for partial-differential equations are widely used in t...
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06/06/2020
Combinatorial Black-Box Optimization with Expert Advice
We consider the problem of black-box function optimization over the bool...
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05/22/2020
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire a...
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05/07/2020
Learning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training i...
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04/30/2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Mode connectivity provides novel geometric insights on analyzing loss la...
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04/02/2020
Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
The recent COVID-19 pandemic has highlighted the need for rapid therapeu...
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02/04/2020
Improving Efficiency in Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...
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12/26/2019