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Found 12 papers, 3 papers with code
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).
Demystifying Inductive Biases for $β$-VAE Based Architectures
By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures.
Clearing the Path for Truly Semantic Representation Learning
The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled.
Neuro-algorithmic Policies for Discrete Planning
Although model-based and model-free approaches to learning control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
Discrete Planning with Neuro-algorithmic Policies
Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.
Sample-efficient Cross-Entropy Method for Real-time Planning
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning
reinforcement-learning
+1
Optimizing Rank-Based Metrics With Blackbox Differentiation
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
Variational Autoencoders Pursue PCA Directions (by Accident)
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.
L4: Practical loss-based stepsize adaptation for deep learning
We propose a stepsize adaptation scheme for stochastic gradient descent.
Efficient Optimization for Rank-based Loss Functions
We provide a complete characterization of the loss functions that are amenable to our algorithm, and show that it includes both AP and NDCG based loss functions.
Total variation on a tree
We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems.
