Search Results for author:
Found 135 papers, 78 papers with code
Composing Molecules with Multiple Property Constraints
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
CapWAP: Image Captioning with a Purpose
In this task, we use question-answer (QA) pairs{---}a natural expression of information need{---}from users, instead of reference captions, for both training and post-inference evaluation.
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69. 5%.
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome.
CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes
Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized.
Dirichlet Flow Matching with Applications to DNA Sequence Design
Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models.
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure.
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more.
Improved motif-scaffolding with SE(3) flow matching
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
Predictive Chemistry Augmented with Text Retrieval
Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature.
Fast non-autoregressive inverse folding with discrete diffusion
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design.
Risk-Controlling Model Selection via Guided Bayesian Optimization
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost.
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time.
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
A significant amount of protein function requires binding small molecules, including enzymatic catalysis.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Improving Protein Optimization with Smoothed Fitness Landscapes
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine.
Conformal Language Modeling
Translating this process to conformal prediction, we calibrate a stopping rule for sampling different outputs from the LM that get added to a growing set of candidates until we are confident that the output set is sufficient.
RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature.
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice.
SE(3) diffusion model with application to protein backbone generation
The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry.
Efficiently Controlling Multiple Risks with Pareto Testing
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses.
Ranked #1 on
Blind Docking
on PDBbind
Calibrated Selective Classification
Providing calibrated uncertainty estimates alongside predictions -- probabilities that correspond to true frequencies -- can be as important as having predictions that are simply accurate on average.
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.
Torsional Diffusion for Molecular Conformer Generation
Molecular conformer generation is a fundamental task in computational chemistry.
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation
Molecular structure recognition is the task of translating a molecular image into its graph structure.
Learning to Split for Automatic Bias Detection
Classifiers are biased when trained on biased datasets.
Conformal Prediction Sets with Limited False Positives
We propose to trade coverage for a notion of precision by enforcing that the presence of incorrect candidates in the predicted conformal sets (i. e., the total number of false positives) is bounded according to a user-specified tolerance.
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
Ranked #5 on
Blind Docking
on PDBBind
Syfer: Neural Obfuscation for Private Data Release
We quantify privacy as the number of attacker guesses required to re-identify a single image (guesswork).
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e. g. drug design or protein engineering.
Fragment-based Sequential Translation for Molecular Optimization
Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities.
Text Style Transfer with Confounders
Existing methods for style transfer operate either with paired sentences or distributionally matched corpora which differ only in the desired style.
Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries
In this paper, we develop a new approach to conformal prediction in which we aim to output a precise set of promising prediction candidates that is guaranteed to contain a limited number of incorrect answers.
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Learning Stable Classifiers by Transferring Unstable Features
Empirical results demonstrate that our algorithm is able to maintain robustness on the target task for both synthetically generated environments and real-world environments.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Learning Graph Models for Template-Free Retrosynthesis
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Ranked #6 on
Single-step retrosynthesis
on USPTO-50k
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
Nutri-bullets Hybrid: Consensual Multi-document Summarization
We present a method for generating comparative summaries that highlight similarities and contradictions in input documents.
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
In this work, we prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes.
Consistent Accelerated Inference via Confident Adaptive Transformers
In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence.
Generating Related Work
Communicating new research ideas involves highlighting similarities and differences with past work.
Nutribullets Hybrid: Multi-document Health Summarization
We present a method for generating comparative summaries that highlights similarities and contradictions in input documents.
Nutri-bullets: Summarizing Health Studies by Composing Segments
We introduce \emph{Nutri-bullets}, a multi-document summarization task for health and nutrition.
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
Typical fact verification models use retrieved written evidence to verify claims.
Few-shot Conformal Prediction with Auxiliary Tasks
We develop a novel approach to conformal prediction when the target task has limited data available for training.
CapWAP: Captioning with a Purpose
In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation.
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity.
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian).
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks.
Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Learning Graph Models for Retrosynthesis Prediction
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Ranked #1 on
Graph Regression
on Lipophilicity
(using extra training data)
Enforcing Predictive Invariance across Structured Biomedical Domains
We evaluate our method on multiple applications: molecular property prediction, protein homology and stability prediction and show that RGM significantly outperforms previous state-of-the-art baselines.
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.
Adaptive Invariance for Molecule Property Prediction
Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale experimental efforts.
Improving Molecular Design by Stochastic Iterative Target Augmentation
The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.
Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
Multi-Objective Molecule Generation using Interpretable Substructures
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
Hierarchical Generation of Molecular Graphs using Structural Motifs
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
Generative Models for Graph-Based Protein Design
Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice.
Capturing Greater Context for Question Generation
Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension.
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
Automatic Fact-guided Sentence Modification
This is a challenging constrained generation task, as the output must be consistent with the new information and fit into the rest of the existing document.
Iterative Target Augmentation for Effective Conditional Generation
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers
This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing.
The Limitations of Stylometry for Detecting Machine-Generated Fake News
Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation.
Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification.
Towards Debiasing Fact Verification Models
Fact verification requires validating a claim in the context of evidence.
Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
In this paper we propose a novel neural approach for automatic decipherment of lost languages.
Hierarchical Graph-to-Graph Translation for Molecules
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties.
Ranked #1 on
Drug Discovery
on QED
Path-Augmented Graph Transformer Network
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations.
Analyzing Learned Molecular Representations for Property Prediction
In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.
Ranked #3 on
Molecular Property Prediction
on QM9
Inferring Which Medical Treatments Work from Reports of Clinical Trials
In this paper, we present a new task and corpus for making this unstructured evidence actionable.
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion.
Cross-lingual zero-shot dependency parsing
Few-Shot Learning
+1
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
GraphIE: A Graph-Based Framework for Information Extraction
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies.
Multi-Source Domain Adaptation with Mixture of Experts
We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources.
Deriving Machine Attention from Human Rationales
Attention-based models are successful when trained on large amounts of data.
The Three Pillars of Machine Programming
In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research.
Junction Tree Variational Autoencoder for Molecular Graph Generation
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Ranked #1 on
Molecular Graph Generation
on InterBioScreen
Using Deep Reinforcement Learning to Generate Rationales for Molecules
Deep learning algorithms are increasingly used in modeling chemical processes.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.
Grounding Language for Transfer in Deep Reinforcement Learning
In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL).
Representation Learning for Grounded Spatial Reasoning
The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment.
Style Transfer from Non-Parallel Text by Cross-Alignment
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
Ranked #7 on
Text Style Transfer
on Yelp Review Dataset (Small)
Deriving Neural Architectures from Sequence and Graph Kernels
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.
Unsupervised Learning of Morphological Forests
This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary.
Aspect-augmented Adversarial Networks for Domain Adaptation
We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain.
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
This paper explores the task of translating natural language queries into regular expressions which embody their meaning.
sk_p: a neural program corrector for MOOCs
We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies.
Rationalizing Neural Predictions
Our approach combines two modular components, generator and encoder, which are trained to operate well together.
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Most successful information extraction systems operate with access to a large collection of documents.
Semi-supervised Question Retrieval with Gated Convolutions
Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions.
Molding CNNs for text: non-linear, non-consecutive convolutions
Moreover, we extend the n-gram convolution to non-consecutive words to recognize patterns with intervening words.
Language Understanding for Text-based Games Using Deep Reinforcement Learning
We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations.
An Unsupervised Method for Uncovering Morphological Chains
In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words.
Automatic Aggregation by Joint Modeling of Aspects and Values
We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries.
Learning to Win by Reading Manuals in a Monte-Carlo Framework
In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance.
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches
We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging.
Content Modeling Using Latent Permutations
We present a novel Bayesian topic model for learning discourse-level document structure.
Learning Document-Level Semantic Properties from Free-Text Annotations
The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews.
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
In this paper, we show that word vector representations can yield significant PP attachment performance gains.
