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Found 22 papers, 10 papers with code
Imitating Graph-Based Planning with Goal-Conditioned Policies
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies.
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts.
Diffusion Probabilistic Models for Graph-Structured Prediction
We also propose a variational expectation maximization algorithm to train our DPM in the semi-supervised setting.
Substructure-Atom Cross Attention for Molecular Representation Learning
Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design.
Learning Debiased Classifier with Biased Committee
We propose a new method for training debiased classifiers with no spurious attribute label.
RoMA: Robust Model Adaptation for Offline Model-based Optimization
We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different
Remarkably, our method is more effective on the challenging low-data and class-imbalanced regimes, and the learned augmentation policy is well-transferable to the different tasks and models.
Spanning Tree-based Graph Generation for Molecules
In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.
Abstract Reasoning via Logic-guided Generation
Abstract reasoning, i. e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.
Self-Improved Retrosynthetic Planning
Our main idea is based on a self-improving procedure that trains the model to imitate successful trajectories found by itself.
Ranked #3 on
Multi-step retrosynthesis
on USPTO-190
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning.
Layer-adaptive sparsity for the Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance.
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Guiding Deep Molecular Optimization with Genetic Exploration
De novo molecular design attempts to search over the chemical space for molecules with the desired property.
Learning What to Defer for Maximum Independent Sets
Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.
Deep Auto-Deferring Policy for Combinatorial Optimization
Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.
Variational Information Distillation for Knowledge Transfer
We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10.
Bucket Renormalization for Approximate Inference
Probabilistic graphical models are a key tool in machine learning applications.
Gauged Mini-Bucket Elimination for Approximate Inference
Recently, so-called gauge transformations were used to improve variational lower bounds on $Z$.
Gauging Variational Inference
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM).
MCMC assisted by Belief Propagation
Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series.
Minimum Weight Perfect Matching via Blossom Belief Propagation
Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM).
