Search Results for author:
Found 17 papers, 13 papers with code
Too Big to Fool: Resisting Deception in Language Models
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses.
The BrowserGym Ecosystem for Web Agent Research
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs).
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents.
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i. e. the use of higher concentration in silico and reduced acquisition time.
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles.
Lookback for Learning to Branch
Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure.
Learning to branch with Tree MDPs
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
Causal Reinforcement Learning using Observational and Interventional Data
We then ask the following questions: can the online and offline experiences be safely combined for learning a causal model ?
Ecole: A Library for Learning Inside MILP Solvers
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers.
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.
Hybrid Models for Learning to Branch
First, in a more realistic setting where only a CPU is available, is the GNN model still competitive?
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets
In this work, we show that the number of parameters can be reduced further to $m^2/n$, in the best case, assuming the label set can be partitioned into $n$ conditionally independent subsets.
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data.
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning
We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC).
