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11/09/2022
Large Language Models with Controllable Working Memory
Large language models (LLMs) have led to a series of breakthroughs in na...
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10/28/2022
When does mixup promote local linearity in learned representations?
Mixup is a regularization technique that artificially produces new sampl...
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03/26/2021
Understanding Robustness of Transformers for Image Classification
Deep Convolutional Neural Networks (CNNs) have long been the architectur...
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02/05/2021
On the Reproducibility of Neural Network Predictions
Standard training techniques for neural networks involve multiple source...
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11/17/2020
Improving Calibration in Deep Metric Learning With Cross-Example Softmax
Modern image retrieval systems increasingly rely on the use of deep neur...
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10/23/2020
Coping with Label Shift via Distributionally Robust Optimisation
The label shift problem refers to the supervised learning setting where ...
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04/23/2020
Doubly-stochastic mining for heterogeneous retrieval
Modern retrieval problems are characterised by training sets with potent...
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12/06/2019
Why ADAM Beats SGD for Attention Models
While stochastic gradient descent (SGD) is still the de facto algorithm ...
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07/02/2018
Semantic Segmentation with Scarce Data
Semantic segmentation is a challenging vision problem that usually neces...
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07/02/2018
How To Backdoor Federated Learning
Federated learning enables multiple participants to jointly construct a ...
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06/17/2018
Learning to Evaluate Image Captioning
Evaluation metrics for image captioning face two challenges. Firstly, co...
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11/30/2017
Convolutional Networks with Adaptive Computation Graphs
Do convolutional networks really need a fixed feed-forward structure? Of...
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11/27/2017
Separating Self-Expression and Visual Content in Hashtag Supervision
The variety, abundance, and structured nature of hashtags make them an i...
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05/30/2017
Deep Learning is Robust to Massive Label Noise
Deep neural networks trained on large supervised datasets have led to im...
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01/06/2017
Learning From Noisy Large-Scale Datasets With Minimal Supervision
We present an approach to effectively use millions of images with noisy ...
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05/20/2016
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
In this work we propose a novel interpretation of residual networks show...
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03/25/2016
Conditional Similarity Networks
What makes images similar? To measure the similarity between images, the...
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01/26/2016
