Search Results for author:
Found 113 papers, 66 papers with code
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
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
Learning Representations that Support Robust Transfer of Predictors
We further demonstrate the impact of optimizing such transfer risk on two controlled settings, each representing a different pattern of environment shift, as well as on two real-world datasets.
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
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.
Poisson Flow Generative Models
We interpret the data points as electrical charges on the $z=0$ hyperplane in a space augmented with an additional dimension $z$, generating a high-dimensional electric field (the gradient of the solution to Poisson equation).
Ranked #31 on
Image Generation
on CIFAR-10
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)
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
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.
Rationalizing Neural Predictions
Our approach combines two modular components, generator and encoder, which are trained to operate well together.
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.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
The new models reduce to PFGM when $D{=}1$ and to diffusion models when $D{\to}\infty$.
Ranked #1 on
Image Generation
on FFHQ 64x64 - 4x upscaling
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
Hierarchical Generation of Molecular Graphs using Structural Motifs
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
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.
Torsional Diffusion for Molecular Conformer Generation
Molecular conformer generation is a fundamental task in computational chemistry.
AlphaFold Meets Flow Matching for Generating Protein Ensembles
When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling.
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.
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.
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.
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
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.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.
Multi-Objective Molecule Generation using Interpretable Substructures
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
Restart Sampling for Improving Generative Processes
Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Subspace Diffusion Generative Models
Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process.
Ranked #19 on
Image Generation
on CIFAR-10
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.
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.
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).
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.
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
We show that the procedure indeed helps in the challenging intermediate regime by reducing (the trace of) the covariance of training targets.
Ranked #12 on
Image Generation
on CIFAR-10
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive.
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.
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.
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)
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.
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
COPAINT also uses the Bayesian framework to jointly modify both revealed and unrevealed regions, but approximates the posterior distribution in a way that allows the errors to gradually drop to zero throughout the denoising steps, thus strongly penalizing any mismatches with the reference image.
Path-Augmented Graph Transformer Network
Much of the recent work on learning molecular representations has been based on Graph Convolution Networks (GCN).
Blank Language Models
We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
Information Obfuscation of Graph Neural Networks
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.
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.
Controlling Directions Orthogonal to a Classifier
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
The Variational Homoencoder: Learning to learn high capacity generative models from few examples
However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables.
Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models has focused on estimating a-posteriori explanations for previously trained models around specific predictions.
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.
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.
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
The runtime of our approach can be amortized at several levels of abstraction, and is particularly favorable for virtual screening settings with a common binding pocket.
The Benefits of Pairwise Discriminators for Adversarial Training
We provide sufficient conditions for local convergence; characterize the capacity balance that should guide the discriminator and generator choices; and construct examples of minimally sufficient discriminators.
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.
Correcting Diffusion Generation through Resampling
Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth images, which has resulted in several notable problems in image generation, including missing object errors in text-to-image generation and low image quality.
Learning Task Informed Abstractions
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features.
Model-based Reinforcement Learning
reinforcement-learning
+1
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.
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.
Direct Optimization through \arg \max for Discrete Variational Auto-Encoder
Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates.
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).
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.
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.
Removing Biases from Molecular Representations via Information Maximization
High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug.
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.
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.
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.
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.
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.
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.
High Dimensional Inference with Random Maximum A-Posteriori Perturbations
This paper shows that the expected value of perturb-max inference with low dimensional perturbations can be used sequentially to generate unbiased samples from the Gibbs distribution.
Learning Optimal Interventions
Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting.
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms
Thus, using the maximum loss over random structured outputs is a principled way of learning the parameter of structured prediction models.
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions.
On the Statistical Efficiency of $\ell_{1,p}$ Multi-Task Learning of Gaussian Graphical Models
In this paper, we present $\ell_{1, p}$ multi-task structure learning for Gaussian graphical models.
Steps Toward Deep Kernel Methods from Infinite Neural Networks
Contemporary deep neural networks exhibit impressive results on practical problems.
CRAFT: ClusteR-specific Assorted Feature selecTion
We present a framework for clustering with cluster-specific feature selection.
Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005)
This is the Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29 2005.
On Measure Concentration of Random Maximum A-Posteriori Perturbations
Applying the general result to MAP perturbations can yield a more efficient algorithm to approximate sampling from the Gibbs distribution.
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions.
Local Aggregative Games
Aggregative games provide a rich abstraction to model strategic multi-agent interactions.
Learning Tree Structured Potential Games
Many real phenomena, including behaviors, involve strategic interactions that can be learned from data.
Controlling privacy in recommender systems
Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences.
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions
In this work we develop efficient methods for learning random MAP predictors for structured label problems.
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes.
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification.
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 Deep Reinforcement Learning to Generate Rationales for Molecules
Deep learning algorithms are increasingly used in modeling chemical processes.
The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
Hierarchical Bayesian methods have the potential to unify many related tasks (e. g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition
An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification.
Strategic Prediction with Latent Aggregative Games
We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions.
Solving graph compression via optimal transport
The transport problem is seeded with prior information about node importance, attributes, and edges in the graph.
Generalization and Representational Limits of Graph Neural Networks
We address two fundamental questions about graph neural networks (GNNs).
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.
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.
Self-Supervised Learning of Appliance Usage
We show that this cross-modal prediction task allows us to detect when a particular appliance is used, and the location of the appliance in the home, all in a self-supervised manner, without any labeled data.
Predicting deliberative outcomes
Our games take as input, e. g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions.
Composing Molecules with Multiple Property Constraints
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
Predicting deliberative outcomes
Our games take as input, e. g., UN resolution to be voted on, and map such contexts to initial strategies, player utilities, and interactions.
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.
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.
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.
Iterative Target Augmentation for Effective Conditional Generation
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
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.
Efficiently Controlling Multiple Risks with Pareto Testing
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time.
Is Conditional Generative Modeling all you need for Decision-Making?
We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills.
GenPhys: From Physical Processes to Generative Models
We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.
Learning Interatomic Potentials at Multiple Scales
When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation.
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.
