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08/08/2023
Continual Pre-Training of Large Language Models: How to (re)warm your model?
Large language models (LLMs) are routinely pre-trained on billions of to...
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06/14/2023
A^2CiD^2: Accelerating Asynchronous Communication in Decentralized Deep Learning
Distributed training of Deep Learning models has been critical to many r...
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06/12/2023
Adversarial Attacks on the Interpretation of Neuron Activation Maximization
The internal functional behavior of trained Deep Neural Networks is noto...
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06/12/2023
Can Forward Gradient Match Backpropagation?
Forward Gradients - the idea of using directional derivatives in forward...
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06/06/2023
Guiding The Last Layer in Federated Learning with Pre-Trained Models
Federated Learning (FL) is an emerging paradigm that allows a model to b...
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04/11/2023
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
In Federated Learning, a global model is learned by aggregating model up...
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04/10/2023
Simulated Annealing in Early Layers Leads to Better Generalization
Recently, a number of iterative learning methods have been introduced to...
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03/26/2023
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
In Continual learning (CL) balancing effective adaptation while combatin...
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02/13/2023
Imitation from Observation With Bootstrapped Contrastive Learning
Imitation from observation (IfO) is a learning paradigm that consists of...
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01/18/2023
Local Learning with Neuron Groups
Traditional deep network training methods optimize a monolithic objectiv...
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10/28/2022
Reliability of CKA as a Similarity Measure in Deep Learning
Comparing learned neural representations in neural networks is a challen...
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03/24/2022
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
Continual Learning research typically focuses on tackling the phenomenon...
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03/24/2022
Text to Mesh Without 3D Supervision Using Limit Subdivision
We present a technique for zero-shot generation of a 3D model using only...
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03/24/2022
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts
Recent work studies the supervised online continual learning setting whe...
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03/08/2022
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In the online continual learning paradigm, agents must learn from a chan...
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01/31/2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets
The development of biologically-plausible learning algorithms is importa...
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01/28/2022
Gradient Masked Averaging for Federated Learning
Federated learning is an emerging paradigm that permits a large number o...
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07/20/2021
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deform...
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06/11/2021
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
The impressive performance of deep convolutional neural networks in sing...
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06/11/2021
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
A commonly cited inefficiency of neural network training using back-prop...
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04/11/2021
Reducing Representation Drift in Online Continual Learning
We study the online continual learning paradigm, where agents must learn...
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01/19/2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel ...
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07/11/2020
Generative Graph Perturbations for Scene Graph Prediction
Inferring objects and their relationships from an image is useful in man...
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05/17/2020
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Scene graph generation (SGG) aims to predict graph-structured descriptio...
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05/10/2020
A Simple and Scalable Shape Representation for 3D Reconstruction
Deep learning applied to the reconstruction of 3D shapes has seen growin...
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04/14/2020
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
The impressive performance of deep convolutional neural networks in sing...
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11/19/2019
Online Learned Continual Compression with Stacked Quantization Module
We introduce and study the problem of Online Continual Compression, wher...
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08/14/2019
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
Embodied Question Answering (EQA) is a recently proposed task, where an ...
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08/11/2019
Online Continual Learning with Maximally Interfered Retrieval
Continual learning, the setting where a learning agent is faced with a n...
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01/23/2019
Decoupled Greedy Learning of CNNs
A commonly cited inefficiency of neural network training by back-propaga...
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12/29/2018
Greedy Layerwise Learning Can Scale to ImageNet
Shallow supervised 1-hidden layer neural networks have a number of favor...
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12/28/2018
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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11/12/2018
Blindfold Baselines for Embodied QA
We explore blindfold (question-only) baselines for Embodied Question Ans...
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09/27/2018
Compressing the Input for CNNs with the First-Order Scattering Transform
We study the first-order scattering transform as a candidate for reducin...
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09/17/2018
Scattering Networks for Hybrid Representation Learning
Scattering networks are a class of designed Convolutional Neural Network...
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03/27/2017
Scaling the Scattering Transform: Deep Hybrid Networks
We use the scattering network as a generic and fixed ini-tialization of ...
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11/17/2016
Fast Non-Parametric Tests of Relative Dependency and Similarity
We introduce two novel non-parametric statistical hypothesis tests. The ...
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05/20/2016
Learning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from obse...
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12/29/2015
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Functional brain networks are well described and estimated from data wit...
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11/14/2015
