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01/11/2023
Continual Few-Shot Learning Using HyperTransformers
We focus on the problem of learning without forgetting from multiple tas...
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01/05/2023
Training trajectories, mini-batch losses and the curious role of the learning rate
Stochastic gradient descent plays a fundamental role in nearly all appli...
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12/15/2022
Transformers learn in-context by gradient descent
Transformers have become the state-of-the-art neural network architectur...
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11/28/2022
Decentralized Learning with Multi-Headed Distillation
Decentralized learning with private data is a central problem in machine...
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03/29/2022
Fine-tuning Image Transformers using Learnable Memory
In this paper we propose augmenting Vision Transformer models with learn...
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01/11/2022
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
In this work we propose a HyperTransformer, a transformer-based model fo...
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07/23/2021
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks
Conditional computation and modular networks have been recently proposed...
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05/07/2021
BasisNet: Two-stage Model Synthesis for Efficient Inference
In this work, we present BasisNet which combines recent advancements in ...
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04/10/2021
Meta-Learning Bidirectional Update Rules
In this paper, we introduce a new type of generalized neural network whe...
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12/10/2020
Large-Scale Generative Data-Free Distillation
Knowledge distillation is one of the most popular and effective techniqu...
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08/11/2020
Image segmentation via Cellular Automata
In this paper, we propose a new approach for building cellular automata ...
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09/07/2019
Non-discriminative data or weak model? On the relative importance of data and model resolution
We explore the question of how the resolution of the input image ("input...
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07/22/2019
Information-Bottleneck Approach to Salient Region Discovery
We propose a new method for learning image attention masks in a semi-sup...
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10/25/2018
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning
We introduce a novel method that enables parameter-efficient transfer an...
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01/13/2018
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
In this paper we describe a new mobile architecture, MobileNetV2, that i...
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01/13/2018
Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation
In this paper we describe a new mobile architecture, MobileNetV2, that i...
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12/08/2017
CycleGAN: a Master of Steganography
CycleGAN is one of the latest successful approaches to learn a correspon...
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02/21/2017
The Power of Sparsity in Convolutional Neural Networks
Deep convolutional networks are well-known for their high computational ...
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06/14/2016