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05/21/2023
Layer Collaboration in the Forward-Forward Algorithm
Backpropagation, which uses the chain rule, is the de-facto standard alg...
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10/12/2022
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Generalization bounds which assess the difference between the true risk ...
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06/12/2022
A Functional Information Perspective on Model Interpretation
Contemporary predictive models are hard to interpret as their deep nets ...
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06/06/2022
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
A key concern in integrating machine learning models in medicine is the ...
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05/03/2022
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Discrete variational auto-encoders (VAEs) are able to represent semantic...
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12/09/2021
Latent Space Explanation by Intervention
The success of deep neural nets heavily relies on their ability to encod...
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11/11/2021
Learning Generalized Gumbel-max Causal Mechanisms
To perform counterfactual reasoning in Structural Causal Models (SCMs), ...
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10/21/2021
Video and Text Matching with Conditioned Embeddings
We present a method for matching a text sentence from a given corpus to ...
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09/12/2021
Mixing between the Cross Entropy and the Expectation Loss Terms
The cross entropy loss is widely used due to its effectiveness and solid...
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04/20/2021
Visual Navigation with Spatial Attention
This work focuses on object goal visual navigation, aiming at finding th...
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03/15/2021
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
This paper proposes an attack-independent (non-adversarial training) tec...
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10/21/2020
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Many recent datasets contain a variety of different data modalities, for...
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07/14/2020
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
As modern neural networks have grown to billions of parameters, meeting ...
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07/11/2020
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
Direct loss minimization is a popular approach for learning predictors o...
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05/05/2020
Generalized Planning With Deep Reinforcement Learning
A hallmark of intelligence is the ability to deduce general principles f...
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02/23/2020
On the generalization of bayesian deep nets for multi-class classification
Generalization bounds which assess the difference between the true risk ...
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01/15/2020
A Formal Approach to Explainability
We regard explanations as a blending of the input sample and the model's...
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06/14/2019
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Direct optimization is an appealing approach to differentiating through ...
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04/11/2019
Factor Graph Attention
Dialog is an effective way to exchange information, but subtle details a...
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04/11/2019
A Simple Baseline for Audio-Visual Scene-Aware Dialog
The recently proposed audio-visual scene-aware dialog task paves the way...
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06/07/2018
Direct Optimization through for Discrete Variational Auto-Encoder
Reparameterization of variational auto-encoders with continuous latent s...
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11/12/2017
High-Order Attention Models for Visual Question Answering
The quest for algorithms that enable cognitive abilities is an important...
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01/31/2017
Co-segmentation for Space-Time Co-located Collections
We present a co-segmentation technique for space-time co-located image c...
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05/23/2016
Online Learning with Feedback Graphs Without the Graphs
We study an online learning framework introduced by Mannor and Shamir (2...
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02/10/2016
High Dimensional Inference with Random Maximum A-Posteriori Perturbations
This paper presents a new approach, called perturb-max, for high-dimensi...
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01/09/2016
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
We present a probabilistic graphical model formulation for the graph clu...
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08/20/2015
Steps Toward Deep Kernel Methods from Infinite Neural Networks
Contemporary deep neural networks exhibit impressive results on practica...
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10/16/2012
Tightening Fractional Covering Upper Bounds on the Partition Function for High-Order Region Graphs
In this paper we present a new approach for tightening upper bounds on t...
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06/27/2012
Efficient Structured Prediction with Latent Variables for General Graphical Models
In this paper we propose a unified framework for structured prediction w...
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06/27/2012