Search Results for author:
Found 12 papers, 4 papers with code
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models.
A Survey of Parallel A*
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games.
Estimating Blink Probability for Highlight Detection in Figure Skating Videos
Highlight detection in sports videos has a broad viewership and huge commercial potential.
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Property prediction plays an important role in material discovery.
Improving Molecular Properties Prediction Through Latent Space Fusion
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
Finding Sub-task Structure with Natural Language Instruction
When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.
