Search Results for author:
Found 36 papers, 8 papers with code
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
The RMSD and barrier height error is further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.
On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem
Our investigation explores how different training instance sizes, embedding dimensions, and distributions influence the outcomes of Unsupervised Learning methods.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes.
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Unsupervised Learning for Solving the Travelling Salesman Problem
Our loss function consists of two parts: one pushes the model to find the shortest path and the other serves as a surrogate for the constraint that the route should form a Hamiltonian Cycle.
Graph Value Iteration
We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances that are out of reach for domain-specialized solvers.
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e. g. 30m, a 100x increase).
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
We find that by using contrastive learning in the supervised setting, we can exploit label information effectively in a data-driven manner, and learn meaningful feature and label embeddings which capture the label correlations and enhance the predictive power.
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
As far as we know, this is the first machine learning method that embeds geographical knowledge in crop yield prediction and predicts the crop yields at county level nationwide.
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
In this paper, we investigate causes for this perceived instability.
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
In particular, as the size of the instance pool increases, the ``hardness gap'' decreases, which facilitates a smoother automated curriculum based learning process.
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
The Fast Kernel Transform
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms.
Materials Representation and Transfer Learning for Multi-Property Prediction
To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
BIG-bench Machine Learning
Generative Adversarial Network
+4
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
In this paper we investigate how RL agents are affected by exchanging the small MLPs with larger modern networks with skip connections and normalization, focusing specifically on actor-critic algorithms.
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning.
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years.
Autonomous synthesis of metastable materials
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery.
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distribution of species across landscapes over time.
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making.
Deep Reasoning Networks: Thinking Fast and Slow, for Pattern De-mixing
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.
Deep Reasoning Networks: Thinking Fast and Slow
At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module.
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa.
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Citizen science projects are successful at gathering rich datasets for various applications.
End-to-End Learning for the Deep Multivariate Probit Model
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities.
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities.
XOR-Sampling for Network Design with Correlated Stochastic Events
In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them.
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data.
Variable Elimination in the Fourier Domain
The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models.
Embed and Project: Discrete Sampling with Universal Hashing
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Many probabilistic inference tasks involve summations over exponentially large sets.
Density Propagation and Improved Bounds on the Partition Function
Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability.
Accelerated Adaptive Markov Chain for Partition Function Computation
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.
