GenAI4ELab/papercli-papers huggingface dataset:
Exploring perceptual straightness in learned visual representations
Authors: Anne Harrington; Vasha DuTell; Ayush Tewari; Mark Hamilton; Simon Stent; Ruth Rosenholtz; William T. Freeman
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Humans have been shown to use a ''straightened'' encoding to represent the natural visual world as it evolves in time (Henaff et al. 2019). In the context of discrete video sequences, ''straightened'' means that changes between frames follow a more linear path in representation space at progressively deeper levels of processing. While deep convolutional networks are often proposed as models of human visual processing, many do not straighten natural videos. In this paper, we explore the relationship between network architecture, differing types of robustness, biologically-inspired filtering mechanisms, and representational straightness in response to time-varying input; we identify strengths and limitations of straightness as a useful way of evaluating neural network representations. We find that (1) adversarial training leads to straighter representations in both CNN and transformer-based architectures but (2) this effect is task-dependent, not generalizing to tasks such as segmentation and frame-prediction, where straight representations are not favorable for predictions; and nor to other types of robustness. In addition, (3) straighter representations impart temporal stability to class predictions, even for out-of-distribution data. Finally, (4) biologically-inspired elements increase straightness in the early stages of a network, but do not guarantee increased straightness in downstream layers of CNNs. We show that straightness is an easily computed measure of representational robustness and stability, as well as a hallmark of human representations with benefits for computer vision models.
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach
Authors: Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Detecting deepfakes remains an open problem. Current detection methods fail against an adversary who adds imperceptible adversarial perturbations to the deepfake to evade detection. We propose Disjoint Deepfake Detection (D3), a deepfake detector designed to improve adversarial robustness beyond de facto solutions such as adversarial training. D3 uses an ensemble of models over disjoint subsets of the frequency spectrum to significantly improve robustness. Our key insight is to leverage a redundancy in the frequency domain and apply a saliency partitioning technique to disjointly distribute frequency components across multiple models. We formally prove that these disjoint ensembles lead to a reduction in the dimensionality of the input subspace where adversarial deepfakes lie. We then empirically validate the D3 method against white-box attacks and black-box attacks and find that D3 significantly outperforms existing state-of-the-art defenses applied to deepfake detection.
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Authors: Sebastian Hofstätter; Jiecao Chen; Karthik Raman; Hamed Zamani
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. Our FiD-Light model constrains the information flow from the encoder (which encodes passages separately) to the decoder (using concatenated encoded representations). Furthermore, we adapt FiD-Light with re-ranking capabilities through textual source pointers, to improve the top-ranked provenance precision. Our experiments on a diverse set of seven knowledge intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier between query latency and effectiveness. FiD-Light with source pointing sets substantial new state-of-the-art results on six KILT tasks for combined text generation and provenance retrieval evaluation, while maintaining reasonable efficiency.
Model-agnostic Measure of Generalization Difficulty
Authors: Akhilan Boopathy; Kevin Liu; Jaedong Hwang; Shu Ge; Asaad Mohammedsaleh; Ila R Fiete
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models. However, quantifying the generalization difficulty of machine learning benchmarks has remained challenging. We propose what is to our knowledge the first model-agnostic measure of the inherent generalization difficulty of tasks. Our inductive bias complexity measure quantifies the total information required to generalize well on a task minus the information provided by the data. It does so by measuring the fractional volume occupied by hypotheses that generalize on a task given that they fit the training data. It scales exponentially with the intrinsic dimensionality of the space over which the model must generalize but only polynomially in resolution per dimension, showing that tasks which require generalizing over many dimensions are drastically more difficult than tasks involving more detail in fewer dimensions. Our measure can be applied to compute and compare supervised learning, reinforcement learning and meta-learning task difficulties against each other. We show that applied empirically, it formally quantifies intuitively expected trends, e.g. that in terms of required inductive bias, MNIST $<$ CIFAR10 $<$ Imagenet and fully observable Markov decision processes (MDPs) $<$ partially observable MDPs. Further, we show that classification of complex images $<$ few-shot meta-learning with simple images. Our measure provides a quantitative metric to guide the construction of more complex tasks requiring greater inductive bias, and thereby encourages the development of more sophisticated architectures and learning algorithms with more powerful generalization capabilities.
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
Authors: Elan Rosenfeld; Pradeep Kumar Ravikumar; Andrej Risteski
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A common explanation for the failure of deep networks to generalize out-of-distribution is that they fail to recover the "correct" features. We challenge this notion with a simple experiment which suggests that ERM already learns sufficient features and that the current bottleneck is not feature learning, but robust regression. We therefore argue that devising simpler methods for learning predictors on existing features is a promising direction for future research. Towards this end, we introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift. Rather than learning one function, DARE performs a domain-specific adjustment to unify the domains in a canonical latent space and learns to predict in this space. Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions. Further, we provide the first finite-environment convergence guarantee to the minimax risk, improving over existing analyses which only yield minimax predictors after an environment threshold. Evaluated on finetuned features, we find that DARE compares favorably to prior methods, consistently achieving equal or better performance.
Bounded Attacks and Robustness in Image Transform Domains
Authors: Mohamed-Hicham LEGHETTAS; Markus Püschel
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Classical image transformation such as the discrete cosine transform (DCT) and the discrete wavelet transforms (DWTs) provide semantically meaningful representations of images. In this paper we propose a general method for adversarial attacks in such transform domains that, in contrast to prior work, obey the $L^\infty$ constraint in the pixel domain. The key idea is to replace the standard attack based on projections with the barrier method. Experiments with DCT and DWTs produce adversarial examples that are significantly more similar to the original than with prior attacks. Further, through adversarial training we show that robustness against our attacks transfers to robustness against a broad class of common image perturbations.
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Authors: Mixue Xie; Shuang Li; Rui Zhang; Chi Harold Liu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue. Despite active DA methods address this by further proposing targetness to measure the representativeness of target domain characteristics, their predictive uncertainty is usually based on the prediction of deterministic models, which can easily be miscalibrated on data with distribution shift. Considering this, we propose a Dirichlet-based Uncertainty Calibration (DUC) approach for active DA, which simultaneously achieves the mitigation of miscalibration and the selection of informative target samples. Specifically, we place a Dirichlet prior on the prediction and interpret the prediction as a distribution on the probability simplex, rather than a point estimate like deterministic models. This manner enables us to consider all possible predictions, mitigating the miscalibration of unilateral prediction. Then a two-round selection strategy based on different uncertainty origins is designed to select target samples that are both representative of target domain and conducive to discriminability. Extensive experiments on cross-domain image classification and semantic segmentation validate the superiority of DUC.
Implicit Neural Spatial Representations for Time-dependent PDEs
Authors: Honglin Chen; Rundi Wu; Eitan Grinspun; Changxi Zheng; Peter Yichen Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Numerically solving partial differential equations (PDEs) often entails spatial and temporal discretizations. Traditional methods (e.g., finite
difference, finite element, smoothed-particle hydrodynamics) frequently adopt explicit spatial discretizations, such as grids, meshes, and point clouds, where each degree-of-freedom corresponds to a location in space. While these explicit spatial correspondences are intuitive to model and understand, these representations are not necessarily optimal for accuracy, memory-usage, or adaptivity. In this work, we explore implicit neural representation as an alternative spatial discretization, where spatial information is implicitly stored in the neural network weights. With implicit neural spatial representation, PDE-constrained time-stepping translates into updating neural network weights, which naturally integrates with commonly adopted optimization time integrators. We validate our approach on a variety of classic PDEs with examples involving large elastic deformations, turbulent fluids, and multiscale phenomena. While slower to compute than traditional representations, our approach exhibits higher accuracy, lower memory consumption, and dynamically adaptive allocation of degrees of freedom without complex remeshing.
PREDICTION OF TOURISM FLOW WITH SPARSE DATA INCORPORATING TOURIST GEOLOCATIONS
Authors: Julian Lemmel; Zahra Babaiee; Marvin Kleinlehner; Ivan Majic; Philipp Neubauer; Johannes Scholz; Radu Grosu; Sophie Neubauer
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Modern tourism in the 21st century is facing numerous challenges. One of these
challenges is the rapidly growing number of tourists in space-limited regions such
as historical city centers, museums, or geographical bottlenecks like narrow val-
leys. In this context, a proper and accurate prediction of tourism volume and
tourism flow within a certain area is important and critical for visitor management
tasks such as sustainable treatment of the environment and prevention of over-
crowding. Static flow control methods like conventional low-level controllers or
limiting access to overcrowded venues could not solve the problem yet. In this
paper, we empirically evaluate the performance of state-of-the-art deep-learning
methods such as RNNs, GNNs, and Transformers as well as the classic statistical
ARIMA method. Granular limited data supplied by a tourism region is extended
by exogenous data such as geolocation trajectories of individual tourists, weather
and holidays. In the field of visitor flow prediction with sparse data, we are thereby
capable of increasing the accuracy of our predictions, incorporating modern input
feature handling as well as mapping geolocation data on top of discrete POI data.
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Authors: Shuai Zhang; Meng Wang; Pin-Yu Chen; Sijia Liu; Songtao Lu; Miao Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include graph sparsification that samples a subgraph to reduce the amount of data aggregation and model sparsification that prunes the neural network to reduce the number of trainable weights. Despite the empirical successes in reducing the training cost while maintaining the test accuracy, the theoretical generalization analysis of sparse learning for GNNs remains elusive. To the best of our knowledge, this paper provides the first theoretical characterization of joint edge-model sparse learning from the perspective of sample complexity and convergence rate in achieving zero generalization error. It proves analytically that both sampling important nodes and pruning neurons with lowest-magnitude can reduce the sample complexity and improve convergence without compromising the test accuracy. Although the analysis is centered on two-layer GNNs with structural constraints on data, the insights are applicable to more general setups and justified by both synthetic and practical citation datasets.
TypeT5: Seq2seq Type Inference using Static Analysis
Authors: Jiayi Wei; Greg Durrett; Isil Dillig
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as a code infilling task by leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for code. Our method uses static analysis to construct dynamic contexts for each code element whose type signature is to be predicted by the model.  We also propose an iterative decoding scheme that incorporates previous type predictions in the model's input context, allowing information exchange between related code elements. Our evaluation shows that the proposed approach, TypeT5, not only achieves a higher overall accuracy (particularly on rare and complex types) but also produces more coherent results with fewer type errors---while enabling easy user intervention.
Skill Machines: Temporal Logic Composition in Reinforcement Learning
Authors: Geraud Nangue Tasse; Devon Jarvis; Steven James; Benjamin Rosman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common approach is to specify tasks through reward machines---finite state machines that encode the task to be solved. We introduce skill machines, a representation that can be learned directly from these reward machines that encode the solution to such tasks. We propose a framework where an agent first learns a set of base skills in a reward-free setting, and then combines these skills with the learned skill machine to produce composite behaviours specified by any regular language, such as linear temporal logics. This provides the agent with the ability to map from complex logical task specifications to near-optimal behaviours zero-shot. We demonstrate our approach in both a tabular and high-dimensional video game environment, where an agent is faced with several of these complex, long-horizon tasks. Our results indicate that the agent is capable of satisfying extremely complex task specifications, producing near optimal performance with no further learning. Finally, we demonstrate that the performance of skill machines can be improved with regular off-policy reinforcement learning algorithms when optimal behaviours are desired.
A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels
Authors: Songhua Wu; Tianyi Zhou; Yuxuan Du; Jun Yu; Bo Han; Tongliang Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Many machine learning algorithms are known to be fragile on simple instance-independent noisy labels. However, noisy labels in real-world data are more devastating since they are produced by more complicated mechanisms in an instance-dependent manner. In this paper, we target this practical challenge of  \textit{Instance-Dependent Noisy Labels} by jointly training 
(1) a model reversely engineering the noise generating mechanism, which produces an \textit{instance-dependent mapping} between the clean label posterior and the observed noisy label; and (2) a robust classifier that produces clean label posteriors. Compared to previous methods, the former model is novel and enables end-to-end learning of the latter directly from noisy labels. An extensive empirical study indicates that the time-consistency of data is critical to the success of training both models and motivates us to develop a curriculum selecting training data based on their dynamics on the two models' outputs over the course of training. We show that the curriculum-selected data provide both clean labels and high-quality input-output pairs for training the two models. Therefore, it leads to promising and robust classification performance even in notably challenging settings of instance-dependent noisy labels where many SoTA methods could easily fail. Extensive experimental comparisons and ablation studies further demonstrate the advantages and significance of the time-consistency curriculum in learning from instance-dependent noisy labels on multiple benchmark datasets.
Diffusing Graph Attention
Authors: Daniel Glickman; Eran Yahav
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The dominant paradigm for machine learning on graphs uses Message Passing Graph Neural Networks~(MP-GNNs), in which node representations are updated by aggregating information in their local neighborhood. Recently, there have been increasingly more attempts to adapt the Transformer architecture to graphs in an effort to solve some known limitations of MP-GNN. A challenging aspect of designing Graph Transformers is integrating the arbitrary graph structure into the architecture. We propose \emph{Graph Diffuser}~(GD) to address this challenge. GD learns to extract structural and positional relationships between distant nodes in the graph, which it then uses to direct the Transformer's attention and node representation. We demonstrate that existing GNNs and Graph Transformers struggle to capture long-range interactions and how Graph Diffuser does so while admitting intuitive visualizations. Experiments on eight benchmarks show Graph Diffuser to be a highly competitive model, outperforming the state-of-the-art in a diverse set of domains.
Generative Modelling with Inverse Heat Dissipation
Authors: Severi Rissanen; Markus Heinonen; Arno Solin
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them.
Safe Reinforcement Learning with Contrastive Risk Prediction
Authors: Hanping Zhang; Yuhong Guo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: As safety violations can lead to severe consequences in real-world applications, the increasing deployment of Reinforcement Learning (RL) in safety-critical domains such as robotics has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches.
Backdoor or Feature? A New Perspective on Data Poisoning
Authors: Alaa Khaddaj; Guillaume Leclerc; Aleksandar Makelov; Kristian Georgiev; Andrew Ilyas; Hadi Salman; Aleksander Madry
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In a backdoor attack, an adversary adds maliciously constructed ("backdoor") examples into a training set to make the resulting model
vulnerable to manipulation. Defending against such attacks---that is, finding and removing the backdoor examples---typically involves viewing these examples as outliers and using techniques from robust statistics to detect and remove them.

In this work, we present a new perspective on backdoor attacks. We argue that without structural information on the training data distribution, backdoor attacks are indistinguishable from naturally-occuring features in the data (and thus impossible to ``detect'' in a general sense). To circumvent this impossibility, we assume that a backdoor attack corresponds to the strongest feature in the training data. Under this assumption---which we make formal---we develop a new framework for detecting backdoor attacks. Our framework naturally gives rise to a corresponding algorithm whose efficacy we show both theoretically and experimentally.