5 citations · 9 across the 2 of their papers we have counts for
6 papers
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Dianbo Liu, Alex Lamb, Xu Ji +4
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically sho…
Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
Xu Ji, Joao Henriques, Tinne Tuytelaars +1
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a me…
There and Back Again: Revisiting Backpropagation Saliency Methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +1
Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a s…
NormGrad: Finding the Pixels that Matter for Training
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +2
The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which…
On the role of neurogenesis in overcoming catastrophic forgetting
German I. Parisi, Xu Ji, Stefan Wermter
Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work,…
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
Xu Ji, João F. Henriques, Andrea Vedaldi
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match…