565 citations · 1.3k across the 8 of their papers we have counts for
8 papers
Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation
Luke Wood, Francois Chollet
Evaluating the COCO mean average precision (MaP) and COCO recall metrics as part of the static computation graph of modern deep learning frameworks poses a unique set of challenges…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Tensor2Tensor for Neural Machine Translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo +10
Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer…
Depthwise Separable Convolutions for Neural Machine Translation
Lukasz Kaiser, Aidan N. Gomez, Francois Chollet
Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown…
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk, François Chollet, Christian Szegedy
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heurist…
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolut…
Information-theoretical label embeddings for large-scale image classification
François Chollet
We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss…
DeepMath - Deep Sequence Models for Premise Selection
Alex A. Alemi, Francois Chollet, Niklas Een +3
We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics. We propose…