Publications

Rhythmic Information Sampling in the Brain during Visual Recognition
Laurent Caplette
Frédéric Gosselin
Towards Compute-Optimal Transfer Learning
Massimo Caccia
Alexandre Galashov
Arthur Douillard
Amal Rannen-Triki
Dushyant Rao
Michela Paganini
Marc'aurelio Ranzato
Razvan Pascanu
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Sitao Luan
Chenqing Hua
Minkai Xu
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Jie Fu
Jure Leskovec
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
DOHA HWANG
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (see more)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.
Equivariance With Learned Canonicalization Functions
Sékou-Oumar Kaba
Arnab Kumar Mondal
Yan Zhang
Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations… (see more). In this paper, we propose an alternative that avoids this architectural constraint by learning to produce a canonical representation of the data. These canonicalization functions can readily be plugged into non-equivariant backbone architectures. We offer explicit ways to implement them for many groups of interest. We show that this approach enjoys universality while providing interpretable insights. Our main hypothesis is that learning a neural network to perform canonicalization is better than doing it using predefined heuristics. Our results show that learning the canonicalization function indeed leads to better results and that the approach achieves great performance in practice.
Graphically Structured Diffusion Models
Christian Dietrich Weilbach
William Harvey
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
Abdurakhmon Sadiev
Marina Danilova
Eduard Gorbunov
Samuel Horváth
Pavel Dvurechensky
Alexander Gasnikov
Peter Richtárik
During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization… (see more) methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as the boundedness of the gradient noise variance or of the objective's gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central
Hyena Hierarchy: Towards Larger Convolutional Language Models
Michael Poli
Stefano Massaroli
Eric Nguyen
Daniel Y Fu
Tri Dao
Stephen Baccus
Stefano Ermon
Christopher Re
Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale. However, the c… (see more)ore building block of Transformers, the attention operator, exhibits quadratic cost in sequence length, limiting the amount of context accessible. Existing subquadratic methods based on low-rank and sparse approximations need to be combined with dense attention layers to match Transformers at scale, indicating a gap in capability. In this work, we propose Hyena, a subquadratic drop-in replacement for attention constructed by interleaving implicitly parametrized long convolutions and data-controlled gating. In challenging reasoning tasks on sequences of thousands to hundreds of thousands of tokens, Hyena improves accuracy by more than 50 points over operators relying on state-space models, transfer functions, and other implicit and explicit methods, matching attention-based models. We set a new state-of-the-art for dense-attention-free architectures on language modeling in standard datasets WikiText103 and The Pile, reaching Transformer quality with a 20% reduction in training compute required at sequence length 2k. Hyena operators are 2x faster than highly optimized attention at sequence length 8k, with speedups of 100x at 64k.
Hyena Hierarchy: Towards Larger Convolutional Language Models
Michael Poli
Stefano Massaroli
Eric Nguyen
Daniel Y Fu
Tri Dao
Stephen Baccus
Stefano Ermon
Christopher Re
Interventional Causal Representation Learning
Kartik Ahuja
Yixin Wang
Divyat Mahajan
Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observa… (see more)tional data and structural assumptions (e.g., conditional independence) to identify the latent factors. However, interventional data is prevalent across applications. Can interventional data facilitate causal representation learning? We explore this question in this paper. The key observation is that interventional data often carries geometric signatures of the latent factors' support (i.e. what values each latent can possibly take). For example, when the latent factors are causally connected, interventions can break the dependency between the intervened latents' support and their ancestors'. Leveraging this fact, we prove that the latent causal factors can be identified up to permutation and scaling given data from perfect
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Sai Rajeswar
Pietro Mazzaglia
Tim Verbelen
Alexandre Piché
Bart Dhoedt
Alexandre Lacoste