Publications

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Yao Feng

Yuliang Xiu

Weiyang Liu

Michael J. Black

Bernhard Schölkopf

The creation of photorealistic virtual worlds requires the accurate modeling of 3D surface geometry for a wide range of objects. For this, m… (see more)eshes are appealing since they 1) enable fast physics-based rendering with realistic material and lighting, 2) support physical simulation, and 3) are memory-efficient for modern graphics pipelines. Recent work on reconstructing and statistically modeling 3D shape, however, has critiqued meshes as being topologically inflexible. To capture a wide range of object shapes, any 3D representation must be able to model solid, watertight, shapes as well as thin, open, surfaces. Recent work has focused on the former, and methods for reconstructing open surfaces do not support fast reconstruction with material and lighting or unconditional generative modelling. Inspired by the observation that open surfaces can be seen as islands floating on watertight surfaces, we parameterize open surfaces by defining a manifold signed distance field on watertight templates. With this parameterization, we further develop a grid-based and differentiable representation that parameterizes both watertight and non-watertight meshes of arbitrary topology. Our new representation, called Ghost-on-the-Shell (G-Shell), enables two important applications: differentiable rasterization-based reconstruction from multiview images and generative modelling of non-watertight meshes. We empirically demonstrate that G-Shell achieves state-of-the-art performance on non-watertight mesh reconstruction and generation tasks, while also performing effectively for watertight meshes.

2024-05-06

International Conference on Learning Representations (Accept (oral))

doi.org

openreview.net

How Connectivity Structure Shapes Rich and Lazy Learning in Neural Circuits

Yuhan Helena Liu

Aristide Baratin

Jonathan Cornford

Stefan Mihalas

Eric Shea-Brown

Guillaume Lajoie

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (see more)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity could exhibit a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights -- in particular their effective rank -- influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Improved baselines for vision-language pre-training

Enrico Fini

Pietro Astolfi

Adriana Romero-Soriano

Jakob Verbeek

Michal Drozdzal

Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved … (see more)impressive results by training on paired image-text data using the contrastive loss. Recent work claims improvements over CLIP using additional non-contrastive losses inspired from self-supervised learning. However, it is sometimes hard to disentangle the contribution of these additional losses from other implementation details, e.g., data augmentation or regularization techniques, used to train the model. To shed light on this matter, in this paper, we first propose, implement and evaluate several baselines obtained by combining contrastive learning with recent advances in self-supervised learning. In particular, we use the loss functions that were proven successful for visual self-supervised learning to align image and text modalities. We find that these baselines outperform a basic implementation of CLIP. However, when a stronger training recipe is employed, the advantage disappears. Indeed, we find that a simple CLIP baseline can also be improved substantially, up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields. Moreover, we discover that it is enough to apply image and text augmentations to make up for most of the improvement attained by prior works. With our improved training recipe for CLIP, we obtain state-of-the-art performance on four standard datasets, and consistently outperform prior work (up to +4% on the largest dataset), while being substantially simpler. The code is available at https://github.com/facebookresearch/clip-rocket

2024-05-06

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Intelligent Switching in Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

Sarath Chandar

In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (see more)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

LEAD: Min-Max Optimization from a Physical Perspective

Reyhane Askari Hemmat

Amartya Mitra

Guillaume Lajoie

Ioannis Mitliagkas

Adversarial formulations have rekindled interest in two-player min-max games. A central obstacle in the optimization of such games is the ro… (see more)tational dynamics that hinder their convergence. In this paper, we show that game optimization shares dynamic properties with particle systems subject to multiple forces, and one can leverage tools from physics to improve optimization dynamics. Inspired by the physical framework, we propose LEAD, an optimizer for min-max games. Next, using Lyapunov stability theory from dynamical systems as well as spectral analysis, we study LEAD’s convergence properties in continuous and discrete time settings for a class of quadratic min-max games to demonstrate linear convergence to the Nash equilibrium. Finally, we empirically evaluate our method on synthetic setups and CIFAR-10 image generation to demonstrate improvements in GAN training.

2024-05-06

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Less or More from Teacher: Exploiting Trilateral Geometry for Knowledge Distillation

Xi Chen

Jun Yan

Boyu Wang

Xue Liu

Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision fro… (see more)m ground truths. However, determining an optimal knowledge fusion ratio that balances these supervisory signals remains challenging. Prior methods generally resort to a constant or heuristic-based fusion ratio, which often falls short of a proper balance. In this study, we introduce a novel adaptive method for learning a sample-wise knowledge fusion ratio, exploiting both the correctness of teacher and student, as well as how well the student mimics the teacher on each sample. Our method naturally leads to the intra-sample trilateral geometric relations among the student prediction (

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency

Tianhong Li

Sangnie Bhardwaj

Yonglong Tian

Han Zhang

Jarred Barber

Dina Katabi

Guillaume Lajoie

Huiwen Chang

Dilip Krishnan

Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalizati… (see more)on capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce

2024-05-06

International Conference on Learning Representations (Accept (spotlight))

doi.org

openreview.net

Local Search GFlowNets

Sungsoo Ahn

Jinkyoo Park

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (see more)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.

2024-05-06

International Conference on Learning Representations (Accept (spotlight))

doi.org

openreview.net

LOQA: Learning with Opponent Q-Learning Awareness

Milad Aghajohari

Juan Agustin Duque

Tim Cooijmans

Aaron Courville

In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to opt… (see more)imize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algorithms have struggled to find equilibria that maximize individual utility while preserving social welfare. In this paper we introduce Learning with Opponent Q-Learning Awareness (LOQA), a novel, decentralized reinforcement learning algorithm tailored to optimizing an agent's individual utility while fostering cooperation among adversaries in partially competitive environments. LOQA assumes the opponent samples actions proportionally to their action-value function Q. Experimental results demonstrate the effectiveness of LOQA at achieving state-of-the-art performance in benchmark scenarios such as the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes with a significantly reduced computational footprint, making it a promising approach for practical multi-agent applications.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Motif: Intrinsic Motivation From Artificial Intelligence Feedback

Roberta Raileanu

Mikael Henaff

Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, … (see more)a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent. Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning. We evaluate Motif's performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif's intrinsic reward with the environment reward, our method significantly outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations. Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

Object-Centric Architectures Enable Efficient Causal Representation Learning

Amin Mansouri

Jason Hartford

Yan Zhang

Yoshua Bengio

Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees… (see more) (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

PhyloGFN: Phylogenetic Inference with Generative Flow Networks

Mingyang Zhou

Nikolay Malkin

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (see more)nd numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. Our code is available at https://github.com/zmy1116/phylogfn.

2024-05-06

International Conference on Learning Representations (Accept (poster))

doi.org

openreview.net

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