Alex Lamb

Towards Improving Exploration through Sibling Augmented GFlowNets.

2025-01-22

ICLR.cc/2025/Conference (poster)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization

Dianbo Liu

Xu Ji

Pascal Notsawo

Michael Curtis Mozer

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (published)

arxiv.org

Leveraging the Third Dimension in Contrastive Learning

Sumukh K Aithal

Michael Curtis Mozer

Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL metho… (see more)ds rely on augmentations obtained by transforming the 2D image pixel map. These augmentations ignore the fact that biological vision takes place in an immersive three-dimensional, temporally contiguous environment, and that low-level biological vision relies heavily on depth cues. Using a signal provided by a pretrained state-of-the-art monocular RGB-to-depth model (the \emph{Depth Prediction Transformer}, Ranftl et al., 2021), we explore two distinct approaches to incorporating depth signals into the SSL framework. First, we evaluate contrastive learning using an RGB+depth input representation. Second, we use the depth signal to generate novel views from slightly different camera positions, thereby producing a 3D augmentation for contrastive learning. We evaluate these two approaches on three different SSL methods -- BYOL, SimSiam, and SwAV -- using ImageNette (10 class subset of ImageNet), ImageNet-100 and ImageNet-1k datasets. We find that both approaches to incorporating depth signals improve the robustness and generalization of the baseline SSL methods, though the first approach (with depth-channel concatenation) is superior. For instance, BYOL with the additional depth channel leads to an increase in downstream classification accuracy from 85.3\% to 88.0\% on ImageNette and 84.1\% to 87.0\% on ImageNet-C.

2023-01-27

ArXiv (preprint)

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Hongyu Zang

Xin Li

Romain Laroche

Remi Tachet des Combes

2022-11-01

ArXiv (preprint)

arxiv.org

Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy

Vikas Verma

Juho Kannala

Alexander Matyasko

Savya Khosla

Adversarial robustness has become a central goal in deep learning, both in theory and in practice. However, successful methods to improve th… (see more)e adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how achieving adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error (when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%.

2022-10-01

Neural Networks (published)

arxiv.org

Coordination Among Neural Modules Through a Shared Global Workspace

Aniket Rajiv Didolkar

Kartikeya Badola

Nan Rosemary Ke

Nasim Rahaman

Jonathan Binas

Charles Blundell

Michael Curtis Mozer

Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For exam… (see more)ple, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are modeled via pairwise interactions: Transformers make use of self-attention to incorporate information from other positions and object-centric architectures make use of graph neural networks to model interactions among entities. We consider how to improve on pairwise interactions in terms of global coordination and a coherent, integrated representation that can be used for downstream tasks. In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information through a common, bandwidth-limited communication channel. We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments. The proposed method includes a shared workspace through which communication among different specialist modules takes place but due to limits on the communication bandwidth, specialist modules must compete for access. We show that capacity limitations have a rational basis in that (1) they encourage specialization and compositionality and (2) they facilitate the synchronization of otherwise independent specialists.

2022-01-28

ICLR.cc/2022/Conference (oral)

Biasly: a machine learning based platform for automatic racial discrimination detection in online texts

David Bamman

Chris Dyer

Noah A. Smith. 2014

Steven Bird

Ewan Klein

Edward Loper

Nat-527

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Kristina Toutanova. 2019

Bert

Samuel Gehman

Suchin Gururangan

Maarten Sap

Dan Hendrycks

Kevin Gimpel. 2020

Gaussian

Di He … (see 22 more)

Guolin Ke

Feng Liao

Mirco Ravanelli

Zhenzhong Lan

Mingda Chen

Sebastian Goodman

Yann Lecun

Bernhard E. Boser

J. Denker

Don-608 nie Henderson

Robin Howard

Wayne Hubbard

Yinhan Liu

Myle Ott

Naman Goyal

Jingfei Du

Mandar Joshi

Danqi Chen

Omer Levy

Mike Lewis

Warning : this paper contains content that may 001 be offensive or upsetting. 002 Detecting hateful, toxic, and otherwise racist 003 or sexi… (see more)st language in user-generated online con-004 tents has become an increasingly important task 005 in recent years. Indeed, the anonymity, the 006 transience, the size of messages, and the dif-007 ficulty of management, facilitate the diffusion 008 of racist or hateful messages across the Inter-009 net. The critical influence of this cyber-racism 010 is no longer limited to social media, but also 011 has a significant effect on our society : corpo-012 rate business operation, users’ health, crimes, 013 etc. Traditional racist speech reporting chan-014 nels have proven inadequate due to the enor-015 mous explosion of information, so there is an 016 urgent need for a method to automatically and 017 promptly detect texts with racial discrimination. 018 We propose in this work, a machine learning-019 based approach to enable automatic detection 020 of racist text content over the internet. State-of-021 the-art machine learning models that are able 022 to grasp language structures are adapted in this 023 study. Our main contribution include 1) a large 024 scale racial discrimination data set collected 025 from three distinct sources and annotated ac-026 cording to a guideline developed by specialists, 027 2) a set of machine learning models with vari-028 ous architectures for racial discrimination de-029 tection, and 3) a web-browser-based software 030 that assist users to debias their texts when us-031 ing the internet. All these resources are made 032 publicly available.

Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Hongyu Zang

Xin Li

Romain Laroche

Remi Tachet des Combes

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and rea… (see more)ch a diverse set of objectives. How to \textit{specify} and \textit{ground} these goals in such a way that we can both reliably reach goals during training as well as generalize to new goals during evaluation remains an open area of research. Defining goals in the space of noisy, high-dimensional sensory inputs is one possibility, yet this poses a challenge for training goal-conditioned agents, or even for generalization to novel goals. We propose to address this by learning compositional representations of goals and processing the resulting representation via a discretization bottleneck, for coarser specification of goals, through an approach we call DGRL. We show that discretizing outputs from goal encoders through a bottleneck can work well in goal-conditioned RL setups, by experimentally evaluating this method on tasks ranging from maze environments to complex robotic navigation and manipulation tasks. Additionally, we show a theoretical result which bounds the expected return for goals not observed during training, while still allowing for specifying goals with expressive combinatorial structure.

Discrete-Valued Neural Communication in Structured Architectures Enhances Generalization

Dianbo Liu

Chen Sun

Michael Curtis Mozer

In this appendix, as a complementary to Theorems 1–2, we provide additional theorems, Theorems 3–4, which further illustrate the two adv… (see more)antages of the discretization process by considering an abstract model with the discretization bottleneck. For the advantage on the sensitivity, the error due to potential noise and perturbation without discretization — the third term ξ(w, r′,M′, d) > 0 in Theorem 4 — is shown to be minimized to zero with discretization in Theorems 3. For the second advantage, the underlying dimensionality of N(M′,d′)(r,H) + ln(N(M,d)(r,Θ)/δ) without discretization (in the bound of Theorem 4) is proven to be reduced to the typically much smaller underlying dimensionality of L + ln(N(M,d)(r, E ×Θ) with discretization in Theorems 3. Here, for any metric space (M, d) and subset M ⊆ M, the r-converging number of M is defined by N(M,d)(r,M) = min { |C| : C ⊆ M,M ⊆ ∪c∈CB(M,d)[c, r]} where the (closed) ball of radius r at centered at c is denoted by B(M,d)[c, r] = {x ∈M : d(x, c) ≤ r}. See Appendix C.1 for a simple comparison between the bound of Theorem 3 and that of Theorem 4 when the metric spaces (M, d) and (M′, d′) are chosen to be Euclidean spaces.

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning

Aniket Rajiv Didolkar

Kshitij Gupta

Nitesh Bharadwaj Gundavarapu

Nan Rosemary Ke

Discrete-Valued Neural Communication

Dianbo Liu

Chen Sun

Michael Curtis Mozer

Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed… (see more) of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a"cat"is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.