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Ramakrishna Vedantam

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9 papers

2 author rows

ICLR Conference 2024 Conference Paper

Embarrassingly Simple Dataset Distillation

Yunzhen Feng
Ramakrishna Vedantam
Julia Kempe

Dataset distillation extracts a small set of synthetic training samples from a large dataset with the goal of achieving competitive performance on test data when trained on this sample. In this work, we tackle dataset distillation at its core by treating it directly as a bilevel optimization problem. Re-examining the foundational back-propagation through time method, we study the pronounced variance in the gradients, computational burden, and long-term dependencies. We introduce an improved method: Random Truncated Backpropagation Through Time (RaT-BPTT) to address them. RaT-BPTT incorporates a truncation coupled with a random window, effectively stabilizing the gradients and speeding up the optimization while covering long dependencies. This allows us to establish new state-of-the-art for a variety of standard dataset benchmarks. A deeper dive into the nature of distilled data unveils pronounced intercorrelation. In particular, subsets of distilled datasets tend to exhibit much worse performance than directly distilled smaller datasets of the same size. Leveraging RaT-BPTT, we devise a boosting mechanism that generates distilled datasets that contain subsets with near optimal performance across different data budgets.

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ICLR Conference 2023 Conference Paper

Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure

Daksh Idnani
Vivek Madan
Naman Goyal 0001
David J. Schwab
Ramakrishna Vedantam

Out of distribution (OoD) generalization has received considerable interest in recent years. In this work, we identify a particular failure mode of OoD generalization for discriminative classifiers that is based on test data (from a new domain) lying in the nullspace of features learnt from source data. We demonstrate the existence of this failure mode across multiple networks trained across RotatedMNIST, PACS, TerraIncognita, DomainNet and ImageNet-R datasets. We then study different choices for characterizing the feature space and show that projecting intermediate representations onto the span of directions that obtain maximum training accuracy provides consistent improvements in OoD performance. Finally, we show that such nullspace behavior also provides an insight into neural networks trained on poisoned data. We hope our work galvanizes interest in the relationship between the nullspace occupancy failure mode and generalization.

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ICML Conference 2023 Conference Paper

Hyperbolic Image-text Representations

Karan Desai
Maximilian Nickel
Tanmay Rajpurohit
Justin Johnson 0001
Ramakrishna Vedantam

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs. Despite being intuitive, current large-scale vision and language models such as CLIP do not explicitly capture such hierarchy. We propose MERU, a contrastive model that yields hyperbolic representations of images and text. Hyperbolic spaces have suitable geometric properties to embed tree-like data, so MERU can better capture the underlying hierarchy in image-text datasets. Our results show that MERU learns a highly interpretable and structured representation space while being competitive with CLIP’s performance on standard multi-modal tasks like image classification and image-text retrieval.

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ICML Conference 2022 Conference Paper

COAT: Measuring Object Compositionality in Emergent Representations

Sirui Xie
Ari S. Morcos
Song-Chun Zhu
Ramakrishna Vedantam

Learning representations that can decompose a multi-object scene into its constituent objects and recompose them flexibly is desirable for object-oriented reasoning and planning. Built upon object masks in the pixel space, existing metrics for objectness can only evaluate generative models with an object-specific “slot” structure. We propose to directly measure compositionality in the representation space as a form of objections, making such evaluations tractable for a wider class of models. Our metric, COAT (Compositional Object Algebra Test), evaluates if a generic representation exhibits certain geometric properties that underpin object compositionality beyond what is already captured by the raw pixel space. Our experiments on the popular CLEVR (Johnson et. al. , 2018) domain reveal that existing disentanglement-based generative models are not as compositional as one might expect, suggesting room for further modeling improvements. We hope our work allows for a unified evaluation of object-centric representations, spanning generative as well as discriminative, self-supervised models.

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NeurIPS Conference 2021 Conference Paper

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers

Ramakrishna Vedantam
David Lopez-Paz
David J. Schwab

Recent work demonstrates that deep neural networks trained using Empirical Risk Minimization (ERM) can generalize under distribution shift, outperforming specialized training algorithms for domain generalization. The goal of this paper is to further understand this phenomenon. In particular, we study the extent to which the seminal domain adaptation theory of Ben-David et al. (2007) explains the performance of ERMs. Perhaps surprisingly, we find that this theory does not provide a tight explanation of the out-of-domain generalization observed across a large number of ERM models trained on three popular domain generalization datasets. This motivates us to investigate other possible measures—that, however, lack theory—which could explain generalization in this setting. Our investigation reveals that measures relating to the Fisher information, predictive entropy, and maximum mean discrepancy are good predictors of the out-of-distribution generalization of ERM models. We hope that our work helps galvanize the community towards building a better understanding of when deep networks trained with ERM generalize out-of-distribution.

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ICML Conference 2021 Conference Paper

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Ramakrishna Vedantam
Arthur Szlam
Maximilian Nickel
Ari S. Morcos
Brenden M. Lake

Humans can learn and reason under substantial uncertainty in a space of infinitely many compositional, productive concepts. For example, if a scene with two blue spheres qualifies as “daxy, ” one can reason that the underlying concept may require scenes to have “only blue spheres” or “only spheres” or “only two objects. ” In contrast, standard benchmarks for compositional reasoning do not explicitly capture a notion of reasoning under uncertainty or evaluate compositional concept acquisition. We introduce a new benchmark, Compositional Reasoning Under Uncertainty (CURI) that instantiates a series of few-shot, meta-learning tasks in a productive concept space to evaluate different aspects of systematic generalization under uncertainty, including splits that test abstract understandings of disentangling, productive generalization, learning boolean operations, variable binding, etc. Importantly, we also contribute a model-independent “compositionality gap” to evaluate the difficulty of generalizing out-of-distribution along each of these axes, allowing objective comparison of the difficulty of each compositional split. Evaluations across a range of modeling choices and splits reveal substantial room for improvement on the proposed benchmark.

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IJCAI Conference 2020 Conference Paper

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

Nirbhay Modhe
Prithvijit Chattopadhyay
Mohit Sharma
Abhishek Das
Devi Parikh
Dhruv Batra
Ramakrishna Vedantam

We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control framework (Gregor et. al. , 2016) which maximizes empowerment -- the ability to reliably reach a diverse set of states and show how to identify sub-goals as states with high necessary option information through an information theoretic regularizer. Despite being discovered without explicit goal supervision, our sub-goals provide better exploration and sample complexity on challenging grid-world navigation tasks compared to supervised counterparts in prior work.

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NeurIPS Conference 2020 Conference Paper

Learning Optimal Representations with the Decodable Information Bottleneck

Yann Dubois
Douwe Kiela
David J. Schwab
Ramakrishna Vedantam

We address the question of characterizing and finding optimal representations for supervised learning. Traditionally, this question has been tackled using the Information Bottleneck, which compresses the inputs while retaining information about the targets, in a decoder-agnostic fashion. In machine learning, however, our goal is not compression but rather generalization, which is intimately linked to the predictive family or decoder of interest (e. g. linear classifier). We propose the Decodable Information Bottleneck (DIB) that considers information retention and compression from the perspective of the desired predictive family. As a result, DIB gives rise to representations that are optimal in terms of expected test performance and can be estimated with guarantees. Empirically, we show that the framework can be used to enforce a small generalization gap on downstream classifiers and to predict the generalization ability of neural networks.

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ICML Conference 2019 Conference Paper

Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering

Ramakrishna Vedantam
Karan Desai
Stefan Lee
Marcus Rohrbach
Dhruv Batra
Devi Parikh

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring less number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.

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