Publications

Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory

Sokhna Diarra Mbacke

Florence Clerc

Pascal Germain

2023-09-21

NeurIPS.cc/2023/Conference (spotlight)

openreview.net

$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning

Adel Nabli

Eugene Belilovsky

Edouard Oyallon

openreview.net

The Impact of Positional Encoding on Length Generalization in Transformers

Amirhossein Kazemnejad

Inkit Padhi

Karthikeyan Natesan

K. Ramamurthy

Payel Das

Siva Reddy

Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the developmen… (voir plus)t of Transformer-based language models. Positional encoding (PE) has been identified as a major factor influencing length generalization, but the exact impact of different PE schemes on extrapolation in downstream tasks remains unclear. In this paper, we conduct a systematic empirical study comparing the length generalization performance of decoder-only Transformers with five different position encoding approaches including Absolute Position Embedding (APE), T5's Relative PE, ALiBi, and Rotary, in addition to Transformers without positional encoding (NoPE). Our evaluation encompasses a battery of reasoning and mathematical tasks. Our findings reveal that the most commonly used positional encoding methods, such as ALiBi, Rotary, and APE, are not well suited for length generalization in downstream tasks. More importantly, NoPE outperforms other explicit positional encoding methods while requiring no additional computation. We theoretically demonstrate that NoPE can represent both absolute and relative PEs, but when trained with SGD, it mostly resembles T5's relative PE attention patterns. Finally, we find that scratchpad is not always helpful to solve length generalization and its format highly impacts the model's performance. Overall, our work suggests that explicit position embeddings are not essential for decoder-only Transformers to generalize well to longer sequences.

openreview.net

Thinker: Learning to Plan and Act

Stephen Chung

Ivan Anokhin

David Scott Krueger

We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a le… (voir plus)arned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform planning by proposing alternative plans to the world model before selecting a final action to execute in the environment. This approach eliminates the need for handcrafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization. We demonstrate the algorithm's effectiveness through experimental results in the game of Sokoban and the Atari 2600 benchmark, where the Thinker algorithm achieves state-of-the-art performance and competitive results, respectively. Visualizations of agents trained with the Thinker algorithm demonstrate that they have learned to plan effectively with the world model to select better actions. Thinker is the first work showing that an RL agent can learn to plan with a learned world model in complex environments.

openreview.net

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

Fuyuan Lyu

Xing Tang

Dugang Liu

Chen Ma

Weihong Luo

Liang Chen

xiuqiang He

Xue (Steve) Liu

openreview.net

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

Fuyuan Lyu

Xing Tang

Dugang Liu

Chen Ma

Weihong Luo

Liang Chen

xiuqiang He

Xue (Steve) Liu

Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, wi… (voir plus)th which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity. In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks. To explore such expansive space, we propose a decomposed space which is calculated on the fly. We then develop a selection algorithm called OptFeature, which efficiently selects the feature interaction from both the feature field and the feature value simultaneously. Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method. All source code are publicly available\footnote{https://anonymous.4open.science/r/OptFeature-Anonymous}.

2023-09-21

NeurIPS.cc/2023/Conference (poster)

openreview.net

A Unified, Scalable Framework for Neural Population Decoding

Mehdi Azabou

Vinam Arora

Venkataramana Ganesh

Ximeng Mao

Santosh B Nachimuthu

Michael Jacob Mendelson

Blake Richards

Matt Perich

Guillaume Lajoie

Eva L Dyer

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model … (voir plus)size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale for neural decoding models.

openreview.net

Versatile Energy-Based Probabilistic Models for High Energy Physics

Taoli Cheng

Aaron Courville

openreview.net

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment

Tianwei Ni

Michel Ma

Benjamin Eysenbach

Pierre-Luc Bacon

Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, an… (voir plus)d determining how actions influence future returns. Both challenges involve modeling long-term dependencies. The Transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain. However, the underlying reason for the strong performance of Transformer-based RL methods remains unclear: is it because they learn effective memory, or because they perform effective credit assignment? After introducing formal definitions of memory length and credit assignment length, we design simple configurable tasks to measure these distinct quantities. Our empirical results reveal that Transformers can enhance the memory capability of RL algorithms, scaling up to tasks that require memorizing observations

openreview.net

Conserving avian evolutionary history can effectively safeguard future benefits for people

Rikki Gumbs

Claudia L. Gray

Michael Hoffmann

Rafael Molina-Venegas

Nisha Owen

Laura J. Pollock

Phylogenetic diversity (PD)—the evolutionary history of a set of species—is conceptually linked to the maintenance of yet-to-be-discover… (voir plus)ed benefits from biodiversity or “option value.” We used global phylogenetic and utilization data for birds to test the PD option value link, under the assumption that the performance of sets of PD-maximizing species at capturing known benefits is analogous to selecting the same species at a point in human history before these benefits were realized. PD performed better than random at capturing utilized bird species across 60% of tests, with performance linked to the phylogenetic dispersion and prevalence of each utilization category. Prioritizing threatened species for conservation by the PD they encapsulate performs comparably to prioritizing by their functional distinctiveness. However, species selected by each metric show low overlap, indicating that we should conserve both components of biodiversity to effectively conserve a variety of uses. Our findings provide empirical support for the link between evolutionary history and benefits for future generations.

2023-09-20

Science Advances (publié)

doi.org

In-Context Learning for Text Classification with Many Labels

Aristides Milios

Siva Reddy

Dzmitry Bahdanau

2023-09-19

ArXiv (prépublication)

doi.org

arxiv.org

M-TAG: A modular teaching-aid for Geant4