Publications

A Generative Model of Symmetry Transformations
James Urquhart Allingham
Bruno Mlodozeniec
Shreyas Padhy
Javier Antoran
David Scott Krueger
Richard E. Turner
Eric Nalisnick
José Miguel Hernández-Lobato
Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though method… (voir plus)s incorporating symmetries often require prior knowledge. While recent advancements have been made in learning those symmetries directly from the dataset, most of this work has focused on the discriminative setting. In this paper, we take inspiration from group theoretic ideas to construct a generative model that explicitly aims to capture the data's approximate symmetries. This results in a model that, given a prespecified broad set of possible symmetries, learns to what extent, if at all, those symmetries are actually present. Our model can be seen as a generative process for data augmentation. We provide a simple algorithm for learning our generative model and empirically demonstrate its ability to capture symmetries under affine and color transformations, in an interpretable way. Combining our symmetry model with standard generative models results in higher marginal test-log-likelihoods and improved data efficiency.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
GenRL: Multimodal-foundation world models for generalization in embodied agents
Pietro Mazzaglia
Tim Verbelen
Bart Dhoedt
Aaron Courville
Sai Rajeswar
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learni… (voir plus)ng (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundation vision-language models (VLMs) generally require fine-tuning or other adaptations to be adopted in embodied contexts, due to the significant domain gap. However, the lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications. In this work, we overcome these problems by presenting multimodal-foundation world models, able to connect and align the representation of foundation VLMs with the latent space of generative world models for RL, without any language annotations. The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain’s dynamics, and learn the corresponding behaviors in imagination. As assessed through large-scale multi-task benchmarking in locomotion and manipulation domains, GenRL enables multi-task generalization from language and visual prompts. Furthermore, by introducing a data-free policy learning strategy, our approach lays the groundwork for foundational policy learning using generative world models. Website, code and data: https://mazpie.github.io/genrl/
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Geometry of naturalistic object representations in recurrent neural network models of working memory
Xiaoxuan Lei
Takuya Ito
Pouya Bashivan
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Grounding Multimodal Large Language Models in Actions
Andrew Szot
Bogdan Mazoure
Harsh Agrawal
(Rex) Devon Hjelm
Zsolt Kira
Alexander T Toshev
Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains including Embodied AI. In this w… (voir plus)ork, we study how to best ground a MLLM into different embodiments and their associated action spaces, including both continuous and discrete actions. For continuous actions, a set of learned tokenizations that capture an action at various resolutions allows for sufficient modeling precision, yielding the best performance on downstream tasks. For discrete actions, semantically aligning these actions with the native output token space of the MLLM leads to the strongest performance. We arrive at these lessons via a thorough study of seven action grounding approaches on five different environments, encompassing over 114 embodied tasks.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
Joseph Cotnareanu
Zhanguang Zhang
Hui-Ling Zhen
Yingxue Zhang
Mark Coates
Efficiently determining the satisfiability of a boolean equation --- known as the SAT problem for brevity --- is crucial in various industri… (voir plus)al problems. Recently, the advent of deep learning methods has introduced significant potential for enhancing SAT solving. However, a major barrier to the advancement of this field has been the scarcity of large, realistic datasets. The majority of current public datasets are either randomly generated or extremely limited, containing only a few examples from unrelated problem families. These datasets are inadequate for meaningful training of deep learning methods. In light of this, researchers have started exploring generative techniques to create data that more accurately reflect SAT problems encountered in practical situations. These methods have so far suffered from either the inability to produce challenging SAT problems or time-scalability obstacles. In this paper we address both by identifying and manipulating the key contributors to a problem's ``hardness'', known as cores. Although some previous work has addressed cores, the time costs are unacceptably high due to the expense of traditional heuristic core detection techniques. We introduce a fast core detection procedure that uses a graph neural network. Our empirical results demonstrate that we can efficiently generate problems that remain hard to solve and retain key attributes of the original example problems. We show via experiment that the generated synthetic SAT problems can be used in a data augmentation setting to provide improved prediction of solver runtimes.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Harnessing small projectors and multiple views for efficient vision pretraining
Arna Ghosh
Kumar Krishna Agrawal
Shagun Sodhani
Adam M. Oberman
Blake Richards
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Philip Fradkin
Puria Azadi Moghadam
Karush Suri
Frederik Wenkel
Ali Bashashati
Maciej Sypetkowski
Dominique Beaini
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellul… (voir plus)ar morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering molecular impact on the cell. In this work, we learn a joint latent space between molecular structures and microscopy phenomic experiments, aligning paired samples with contrastive learning. Specifically, we study the problem ofContrastive PhenoMolecular Retrieval, which consists of zero-shot molecular structure identification conditioned on phenomic experiments. We assess challenges in multi-modal learning of phenomics and molecular modalities such as experimental batch effect, inactive molecule perturbations, and encoding perturbation concentration. We demonstrate improved multi-modal learner retrieval through (1) a uni-modal pre-trained phenomics model, (2) a novel inter sample similarity aware loss, and (3) models conditioned on a representation of molecular concentration. Following this recipe, we propose MolPhenix, a molecular phenomics model. MolPhenix leverages a pre-trained phenomics model to demonstrate significant performance gains across perturbation concentrations, molecular scaffolds, and activity thresholds. In particular, we demonstrate an 8.1x improvement in zero shot molecular retrieval of active molecules over the previous state-of-the-art, reaching 77.33% in top-1% accuracy. These results open the door for machine learning to be applied in virtual phenomics screening, which can significantly benefit drug discovery applications.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada
Pietro Astolfi
Melissa Hall
Reyhane Askari Hemmat
Yohann Benchetrit
Marton Havasi
Matthew J. Muckley
Karteek Alahari
Adriana Romero Soriano
Jakob Verbeek
Michal Drozdzal
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, large-scale end-to-e… (voir plus)nd training of these models is computationally costly, and hence most research focuses either on finetuning pretrained models or experiments at smaller scales. In this work we aim to improve the training efficiency and performance of LDMs with the goal of scaling to larger datasets and higher resolutions. We focus our study on two points that are critical for good performance and efficient training: (i) the mechanisms used for semantic level (\eg a text prompt, or class name) and low-level (crop size, random flip, \etc) conditioning of the model, and (ii) pre-training strategies to transfer representations learned on smaller and lower-resolution datasets to larger ones. The main contributions of our work are the following: we present systematic experimental study of these points, we propose a novel conditioning mechanism that disentangles semantic and low-level conditioning, we obtain state-of-the-art performance on CC12M for text-to-image at 512 resolution.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (voir plus)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at [this link](https://github.com/GFNOrg/gfn-diffusion) as a base for future work on diffusion models for amortized inference.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
Improving Context-Aware Preference Modeling for Language Models
Silviu Pitis
Ziang Xiao
Nicolas Le Roux
Alessandro Sordoni
While finetuning language models from pairwise preferences has proven remarkably effective, the underspecified nature of natural language pr… (voir plus)esents critical challenges. Direct preference feedback is uninterpretable, difficult to provide where multidimensional criteria may apply, and often inconsistent, either because it is based on incomplete instructions or provided by diverse principals. To address these challenges, we consider the two-step preference modeling procedure that first resolves the under-specification by selecting a context, and then evaluates preference with respect to the chosen context. We decompose reward modeling error according to these two steps, which suggests that supervising context in addition to context-specific preference may be a viable approach to aligning models with diverse human preferences. For this to work, the ability of models to evaluate context-specific preference is critical. To this end, we contribute context-conditioned preference datasets and accompanying experiments that investigate the ability of language models to evaluate context-specific preference. Unlike past datasets, where context-specific preference is highly correlated with general preference, our "preference reversal" datasets disentangle context-specific and general preferences to isolate context-specific capabilities. We use our datasets to (1) show that existing preference models benefit from, but fail to fully consider, added context, (2) finetune a context-aware reward model with context-specific performance exceeding that of GPT-4 and Llama 3 70B, and (3) investigate the potential value of context-aware preference modeling.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Hongyao Tang
Glen Berseth
Deep neural networks provide Reinforcement Learning (RL) powerful function approximators to address large-scale decision-making problems. Ho… (voir plus)wever, these approximators introduce challenges due to the non-stationary nature of RL training. One source of the challenges in RL is that output predictions can churn, leading to uncontrolled changes after each batch update for states not included in the batch. Although such a churn phenomenon exists in each step of network training, how churn occurs and impacts RL remains under-explored. In this work, we start by characterizing churn in a view of Generalized Policy Iteration with function approximation, and we discover a chain effect of churn that leads to a cycle where the churns in value estimation and policy improvement compound and bias the learning dynamics throughout the iteration. Further, we concretize the study and focus on the learning issues caused by the chain effect in different settings, including greedy action deviation in value-based methods, trust region violation in proximal policy optimization, and dual bias of policy value in actor-critic methods. We then propose a method to reduce the chain effect across different settings, called Churn Approximated ReductIoN (CHAIN), which can be easily plugged into most existing DRL algorithms. Our experiments demonstrate the effectiveness of our method in both reducing churn and improving learning performance across online and offline, value-based and policy-based RL settings, as well as a scaling setting.
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Interpreting Learned Feedback Patterns in Large Language Models
Luke Marks
Amir Abdullah
Clement Neo
Rauno Arike
David Scott Krueger
Philip Torr
Fazl Barez
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net