Publications

Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Maxwell J. Farrell
Stefano Recanatesi
Timothy Moore
Guillaume Lajoie
Eric Todd SheaBrown
2022-10-19
Nature Machine Intelligence (publié)
doi.org
Causal inference from text: A commentary
Dhanya Sridhar
David Blei
2022-10-19
Science Advances (publié)
doi.org
Aligning MAGMA by Few-Shot Learning and Finetuning
Jean-Charles Layoun
Alexis Roger
Irina Rish
2022-10-18
ArXiv (prépublication)
doi.org
arxiv.org
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu
Chen Ma
Yingxue Zhang
Xue (Steve) Liu
Ruiming Tang
Mark Coates
2022-10-17
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)
doi.org
arxiv.org
Lifelong Online Learning from Accumulated Knowledge
Changjian Shui
William Wang
Ihsen Hedhli
Chi Man Wong
Feng Wan
Boyu Wang
Christian Gagné
In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (voir plus) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.
2022-10-17
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu
Xing Tang
Hong Zhu
Huifeng Guo
Yingxue Zhang
Ruiming Tang
Xue (Steve) Liu
Click-through rate (CTR) prediction model usually consists of three components: embedding table, feature interaction layer, and classifier. … (voir plus)Learning embedding table plays a fundamental role in CTR prediction from the view of the model performance and memory usage. The embedding table is a two-dimensional tensor, with its axes indicating the number of feature values and the embedding dimension, respectively. To learn an efficient and effective embedding table, recent works either assign various embedding dimensions for feature fields and reduce the number of embeddings respectively or mask the embedding table parameters. However, all these existing works cannot get an optimal embedding table. On the one hand, various embedding dimensions still require a large amount of memory due to the vast number of features in the dataset. On the other hand, decreasing the number of embeddings usually suffers from performance degradation, which is intolerable in CTR prediction. Finally, pruning embedding parameters will lead to a sparse embedding table, which is hard to be deployed. To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. Specifically, we propose pruning the redundant embeddings regarding corresponding features' importance by learnable pruning thresholds. Furthermore, we consider assigning various embedding dimensions as one single candidate architecture. To efficiently search the optimal embedding dimensions, we design a uniform embedding dimension sampling scheme to equally train all candidate architectures, meaning architecture-related parameters and learnable thresholds are trained simultaneously in one supernet. We then propose an evolution search method based on the supernet to find the optimal embedding dimensions for each field. Experiments on public datasets show that OptEmbed can learn a compact embedding table which can further improve the model performance.
2022-10-17
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)
doi.org
arxiv.org
Inductive biases for deep learning of higher-level cognition
Anirudh Goyal
Yoshua Bengio
2022-10-12
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (publié)
doi.org
arxiv.org
Lookback for Learning to Branch
Prateek Gupta
Elias Boutros Khalil
Didier Chételat
Maxime Gasse
Yoshua Bengio
Andrea Lodi
M. Pawan Kumar
2022-10-10
TMLR (accepté)
doi.org
openreview.net
Dissecting adaptive methods in GANs
Samy Jelassi
David Dobre
Arthur Mensch
Yuanzhi Li
Gauthier Gidel
Adaptive methods are a crucial component widely used for training generative adversarial networks (GANs). While there has been some work to … (voir plus)pinpoint the “marginal value of adaptive methods” in standard tasks, it remains unclear why they are still critical for GAN training. In this paper, we formally study how adaptive methods help train GANs; inspired by the grafting method proposed in Agarwal et al. (2020), we separate the magnitude and direction components of the Adam updates, and graft them to the direction and magnitude of SGDA updates respectively. By considering an update rule with the magnitude of the Adam update and the normalized direction of SGD, we empirically show that the adaptive magnitude of Adam is key for GAN training. This motivates us to have a closer look at the class of normalized stochastic gradient descent ascent (nSGDA) methods in the context of GAN training. We propose a synthetic theoretical framework to compare the performance of nSGDA and SGDA for GAN training with neural networks. We prove that in that setting, GANs trained with nSGDA recover all the modes of the true distribution, whereas the same networks trained with SGDA (and any learning rate configuration) suffer from mode collapse. The critical insight in our analysis is that normalizing the gradients forces the discriminator and generator to be updated at the same pace. We also experimentally show that for several datasets, Adam’s performance can be recovered with nSGDA methods.
2022-10-09
ArXiv (prépublication)
doi.org
openreview.net
PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors
Hung-Yang Chang
Seyyed Hasan Mozafari
Cheng Chen
James J. Clark
Brett Meyer
Warren Gross
2022-10-08
Journal of Signal Processing Systems (publié)
doi.org
Novice Type Error Diagnosis with Natural Language Models
Chuqin Geng
Haolin Ye
Yixuan Li
Tianyu Han
Brigitte Pientka
Xujie Si
Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations. However, this flexibili… (voir plus)ty makes it highly non-trivial to diagnose ill-typed programs, especially for novice programmers. Compared to classic constraint solving and optimization-based approaches, the data-driven approach has shown great promise in identifying the root causes of type errors with higher accuracy. Instead of relying on hand-engineered features, this work explores natural language models for type error localization, which can be trained in an end-to-end fashion without requiring any features. We demonstrate that, for novice type error diagnosis, the language model-based approach significantly outperforms the previous state-of-the-art data-driven approach. Specifically, our model could predict type errors correctly 62% of the time, outperforming the state-of-the-art Nate's data-driven model by 11%, in a more rigorous accuracy metric. Furthermore, we also apply structural probes to explain the performance difference between different language models.
2022-10-07
ArXiv (prépublication)
doi.org
arxiv.org
Functional connectivity subtypes associate robustly with ASD diagnosis
Sebastian G. W. Urchs
Angela Tam
Pierre Orban
Clara A. Moreau
Yassine Benhajali
Hien Duy Nguyen
Alan C. Evans
Pierre (Louis) Bellec
Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes th… (voir plus)is neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.
2022-10-04
eLife (publié)
doi.org