Search Results for author:
Found 8 papers, 3 papers with code
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
Conditional sound separation in multi-source audio mixtures without having access to single source sound data during training is a long standing challenge.
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception.
PDP: A General Neural Framework for Learning SAT Solvers
There have been recent efforts for incorporating Graph Neural Network models for learning fully neural solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT).
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
In this paper, we propose a generic neural framework for learning CSP solvers that can be described in terms of probabilistic inference and yet learn search strategies beyond greedy search.
The Bregman Variational Dual-Tree Framework
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning.
