GenAI4ELab/papercli-papers huggingface dataset:
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
Authors: Paul F Jaeger; Carsten Tim Lüth; Lukas Klein; Till J. Bungert
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying the model's predictive uncertainty, learning explicit scoring functions, or assessing whether the input is in line with the training distribution. Curiously, while these approaches all state to address the same eventual goal of detecting failures of a classifier upon real-world application, they currently constitute largely separated research fields with individual evaluation protocols, which either exclude a substantial part of relevant methods or ignore large parts of relevant failure sources. In this work, we systematically reveal current pitfalls caused by these inconsistencies and derive requirements for a holistic and realistic evaluation of failure detection. To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w.r.t all relevant methods and failure sources. The revelation of a simple softmax response baseline as the overall best performing method underlines the drastic shortcomings of current evaluation in the plethora of publicized research on confidence scoring. Code and trained models are at https://github.com/https://github.com/IML-DKFZ/fd-shifts
Consistent and Truthful Interpretation with Fourier Analysis
Authors: Yifan Zhang; Haowei He; Yang Yuan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: For many interdisciplinary fields, 
ML interpretations need to be consistent with \emph{what-if} scenarios related to the current case, i.e., if one factor changes, how does the model react?
Although the attribution methods are supported by the elegant axiomatic systems, they mainly focus on individual inputs, 
and are generally inconsistent. 
To support what-if scenarios, we introduce a new objective of consistency based on a notion called truthful interpretation. Towards this objective, 
we apply Fourier analysis of Boolean functions to get 
consistency guarantees. 
Experimental results show that 
for neighborhoods with various radii, 
our method achieves $2$x - $50$x lower inconsistency compared with the other methods.
Temporal Change Sensitive Representation for Reinforcement Learing
Authors: Qi Gao; Wei Xu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Image-based deep reinforcement learning has made a great improvement recently by combining state-of-the-art reinforcement learning algorithms with self-supervised representation learning algorithms. However, these self-supervised representation learning algorithms are designed to preserve global visual information, which may miss changes in visual information that are important for performing the task. To resolve this problem, self-supervised representation learning specifically designed for better preserving task relevant information is necessary. Following this idea, we introduce Temporal Change Sensitive Representation (TCSR), which is designed for reinforcement learning algorithms that have a latent dynamic model. TCSR enforces the latent state representation of the reinforcement agent to put more emphasis on the part of observation that could potentially change in the future. Our method achieves SoTA performance in Atari100K benchmark.
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path
Authors: Yihan Du; Siwei Wang; Longbo Huang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk avoidance throughout the decision process, such as autonomous driving, clinical treatment planning and robotics. We investigate two performance metrics under Iterated CVaR RL, i.e., Regret Minimization  and Best Policy Identification. For both metrics, we design efficient algorithms ICVaR-RM and ICVaR-BPI, respectively, and provide nearly matching upper and lower bounds with respect to the number of episodes $K$. We also investigate an interesting limiting case of Iterated CVaR RL, called Worst Path RL, where the objective becomes to maximize the minimum possible cumulative reward. For Worst Path RL, we propose an efficient algorithm with constant upper and lower bounds. Finally, the techniques we develop for bounding the change of CVaR due to the value function shift and decomposing the regret via a distorted visitation distribution are novel, and can find applications in other risk-sensitive online learning problems.
Transmission Dynamics of Hepatitis B: Analysis and Control
Authors: Tahir Khan; zhiqiang xu; Xin Cao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The infection of hepatitis B attacks the liver and can produce acute and chronic diseases, while it is a major health problem and life-threatening around the globe. The control of this infection is a difficult task due to several reasons such as variation of human behavior, proper medication, vaccination, and existence of a large number of carries, etc., but understanding the dynamics of the infection helps to design appropriate control strategies. Thus, a proper complex dynamical system is needed to find the stability conditions and propose intervention strategies for forecasting the control of hepatitis B virus transmission. We formulate a model that will be helpful to investigate the temporal dynamics and suggest control strategies for hepatitis B infection. The well-posedness of the proposed model will be shown, and used to find the threshold parameter to analyze the model equilibria and its stability. We also perform the sensitive analysis of the threshold quantity to quantify the most sensitive epidemic parameters. Based on the temporal dynamics and sensitivity, we investigate effective methods to minimize the infection of hepatitis B, and develop the algorithms to support the theoretical results with the help of numerical simulations.
Context-Aware Image Completion
Authors: Jinoh Cho; Minguk Kang; Vibhav Vineet; Jaesik Park
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Image completion is a task that aims to fill in the missing region of a masked image with plausible contents. However, existing image completion methods tend to fill in the missing region with the surrounding texture instead of hallucinating a visual instance that is suitable in accordance with the context of the scene. In this work, we propose a novel image completion model, dubbed Refill, that hallucinates the missing instance that harmonizes well with - and thus preserves - the original context. Refill first adopts a transformer architecture that considers the types, locations of the visible instances, and the location of the missing region. Then, Refill completes the missing foreground and background semantic segmentation masks within the missing region, providing pixel-level semantic and structural guidance to generate missing contents with seamless boundaries. Finally, we condition the image synthesis blocks by using the completed segmentation mask to generate photo-realistic contents to fill out the missing region. Experimental results show the superiority of Refill over state-of-the-art image completion approaches on various natural images.
Pruning with Output Error Minimization for Producing Efficient Neural Networks
Authors: Koji Kamma; Toshikazu Wada
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: DNNs are known to have excessive parameters and thus are computationally expensive, which poses a challenge for implementations in various applications. Structured pruning is a technique of compressing a trained DNN model by removing redundant neurons (or channels). How well a pruned model maintains its accuracy depends on two factors. The first is compression ratio optimization, in other words, how many neurons are reduced in each layer. The other is layer-wise optimization, in other words, which neurons to be preserved in each layer. In this paper, we propose Pruning with Output Error Minimization (POEM), a layer-wise pruning method that conducts pruning and then performs reconstruction to compensate the error caused by pruning. The strength of POEM lies in its reconstruction using the weighted least squares method so as to minimize the output error of the activation function, while the previous methods minimize the error of the value before applying the activation function. The experiments with well-known DNN models and a large scale image recognition dataset show that POEM is better than the previous methods in maintaining the accuracy of those models.
The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection
Authors: Griffin Floto; Stefan Kremer; Mihai Nica
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A problem with using the Gaussian distribution as a prior for the variational autoencoder (VAE) is that the set on which Gaussians have high probability density is small as the latent dimension increases. This is an issue because VAEs try to attain both a high likelihood with respect to a prior distribution and at the same time, separation between points for better reconstruction. Therefore, a small volume in the high-density region of the prior is problematic because it restricts the separation of latent points.  To ameliorate this, we propose a simple generalization of the Gaussian distribution, called the tilted Gaussian, which has a maximum probability density occurring on a sphere instead of a single point. The tilted Gaussian has exponentially more volume in high-density regions than the standard Gaussian as a function of the distribution dimension. We empirically demonstrate that this simple change in the prior distribution improves VAE performance on the task of detecting unsupervised out-of-distribution (OOD) samples. We also introduce a new OOD testing procedure, called the Will-It-Move test, where the tilted Gaussian achieves remarkable OOD performance.
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
Authors: Runqi Lin; Chaojian Yu; Tongliang Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Single-step adversarial training (SSAT) is shown to be able to defend against iterative-step adversarial attacks to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO) with strong adversaries, showing that the classifier decision boundaries are highly distorted and robust accuracy against iterative-step adversarial attacks suddenly drops from peak to nearly 0% in a few epochs. In this work, we find that some adversarial examples generated on the network trained by SSAT exhibit anomalous behaviour, that is, although the training data is generated by the inner maximization process, the loss of some adversarial examples decreases instead, which we called abnormal adversarial examples. Furthermore, network optimization on these abnormal adversarial examples will further accelerate the model decision boundaries distortion, and correspondingly, the number of abnormal adversarial examples will sharply increase with CO. These observations motivate us to prevent CO by hindering the generation of abnormal adversarial examples. Specifically, we design a novel method, Abnormal Adversarial Examples Regularization (AAER), which explicitly regularizes the number and logits variation of abnormal adversarial examples to hinder the model from generating abnormal adversarial examples. Extensive experiments demonstrate that our method can prevent CO and further boost adversarial robustness with strong adversaries.
The Game of Hidden Rules: A New Challenge for Machine Learning
Authors: Eric Pulick; Shubham Kumar Bharti; Yiding Chen; Vladimir Menkov; Yonatan Dov Mintz; Paul Kantor; Vicki M. Bier
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Systematic examination of learning tasks remains an important but understudied area of machine learning (ML) research. To date, most ML research has focused on measuring performance on new tasks or surpassing state of the art performance on existing tasks. These efforts are vital but do not explain why some tasks are more difficult than others. Understanding how task characteristics affect difficulty is critical to formalizing ML's strengths and limitations; a rigorous assessment of which types of tasks are well-suited to a specific algorithm and, conversely, which algorithms are well-suited to a specific task would mark an important step forward for the field. To assist researchers in this effort, we introduce a novel learning environment designed to study how task characteristics affect measured difficulty for the learner. This tool frames learning tasks as a ``board-clearing game,'' which we call the Game of Hidden Rules (GOHR). In each instance of the game, the researcher encodes a specific rule, unknown to the learner, that determines which moves are allowed at each state of the game. The learner must infer the rule through play. We detail the game's expressive rule syntax and show how it gives researchers granular control over learning tasks. We present sample rules, a sample ML algorithm, and methods to assess algorithm performance. Separately, we provide additional benchmark rules, a public leaderboard for performance on these rules, and documentation for installing and using the GOHR environment.
Learning with Auxiliary Activation for Memory-Efficient Training
Authors: Sunghyeon Woo; Dongsuk Jeon
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: While deep learning has achieved great success in various fields, a large amount of memory is necessary to train deep neural networks, which hinders the development of massive state-of-the-art models. The reason is the conventional learning rule, backpropagation, should temporarily store input activations of all the layers in the network. To overcome this, recent studies suggested various memory-efficient implementations of backpropagation. However, those approaches incur computational overhead due to the recomputation of activations, slowing down neural network training. In this work, we propose a new learning rule which significantly reduces memory requirements while closely matching the performance of backpropagation. The algorithm combines auxiliary activation with output activation during forward propagation, while only auxiliary activation is used during backward propagation instead of actual input activation to reduce the amount of data to be temporarily stored. We mathematically show that our learning rule can reliably train the networks whose loss landscape is convex if the auxiliary activation satisfies certain conditions. Based on this observation, we suggest candidates of auxiliary activation that satisfy those conditions. Experimental results confirm that the proposed learning rule achieves competitive performance compared to backpropagation in various models such as ResNet, Transformer, BERT, ViT, and MLP-Mixer.
What Matters In The Structured Pruning of Generative Language Models?
Authors: Michael Santacroce; Zixin Wen; yelong shen; Weizhu Chen; Yuanzhi Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Auto-regressive large language models such as GPT-3 require enormous computational resources to use, leading to huge financial cost and environmental impact. Structured pruning methods traditionally reduce resource usage, however, their application to and efficacy for generative language models is heavily under-explored. We analyze the effects of magnitude, random, and movement (Lagunas et al., 2021) pruning on MLP layers in GPT-like models. We find that movement can under-perform for these models while random pruning nearly matches the best methods. By examining neuron-level redundancy measures, we discover that movement does not select neurons based on how unique they are compared to other neurons, leaving behind excess redundancy. In view of this, we introduce Globally Unique Movement (GUM) to select neurons based on both uniqueness and sensitivity. We then discuss the roles of our techniques on different redundancy metrics through careful comparisons and ablations.
GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis
Authors: Zhenhui Ye; Ziyue Jiang; Yi Ren; Jinglin Liu; Jinzheng He; Zhou Zhao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Generating photo-realistic video portraits with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field (NeRF) in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods is limited by the small scale of training data. In this work, we propose GeneFace, a generalized and high-fidelity NeRF-based talking face generation method, which can generate natural results corresponding to various out-of-domain audio. Specifically, we learn a variational motion generator on a large lip-reading corpus, and introduce a domain adaptative post-net to calibrate the result. Moreover, we learn a NeRF-based renderer conditioned on the predicted motion. A head-aware torso-NeRF is proposed to eliminate the head-torso separation problem. Extensive experiments show that our method achieves more generalized and high-fidelity talking face generation compared to previous methods. Video samples and source code are available at https://geneface.github.io .
Weighted Clock Logic Point Process
Authors: Ruixuan Yan; Yunshi Wen; Debarun Bhattacharjya; Ronny Luss; Tengfei Ma; Achille Fokoue; Anak Agung Julius
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Datasets involving multivariate event streams are prevalent in numerous applications. We present a novel framework for modeling temporal point processes called clock logic neural networks (CLNN) which learn weighted clock logic (wCL) formulas as interpretable temporal rules by which some events promote or inhibit other events. Specifically, CLNN models temporal relations between events using conditional intensity rates informed by a set of wCL formulas, which are more expressive than related prior work. Unlike conventional approaches of searching for generative rules through expensive combinatorial optimization, we design smooth activation functions for components of wCL formulas that enable a continuous relaxation of the discrete search space and efficient learning of wCL formulas using gradient-based methods. Experiments on synthetic datasets manifest our model's ability to recover the ground-truth rules and improve computational efficiency. In addition, experiments on real-world datasets show that our models perform competitively when compared with state-of-the-art models.
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Authors: Muthu Chidambaram; Xiang Wang; Chenwei Wu; Rong Ge
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primitive used in the training of state-of-the-art image classification models due to its demonstrated benefits over empirical risk minimization with regards to generalization and robustness. In this work, we try to explain some of this success from a feature learning perspective. We focus our attention on classification problems in which each class may have multiple associated features (or views) that can be used to predict the class correctly. Our main theoretical results demonstrate that, for a non-trivial class of data distributions with two features per class, training a 2-layer convolutional network using empirical risk minimization can lead to learning only one feature for almost all classes while training with a specific instantiation of Mixup succeeds in learning both features for every class. We also show empirically that these theoretical insights extend to the practical settings of image benchmarks modified to have additional synthetic features.
Backdoor Mitigation by Correcting Activation Distribution Alteration
Authors: Xi Li; Zhen Xiang; George Kesidis; Bo Li; David J. Miller
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Backdoor (Trojan) attacks are an important type of adversarial exploit against deep neural networks (DNNs), wherein a test instance is (mis)classified to the attacker's target class whenever a backdoor trigger is present. In this paper, we reveal and analyze an important property of backdoor attacks: a successful attack causes an alteration in the distribution of internal layer activations for backdoor-trigger instances, compared to that for clean instances. Even more importantly, we find that instances with the backdoor trigger will be correctly classified to their original source classes if this distribution alteration is reversed. Based on our observations, we propose an efficient and effective method that achieves post-training backdoor mitigation by correcting the distribution alteration using reverse-engineered triggers. Notably, our method does not change any trainable parameters of the DNN, but achieves generally better mitigation performance than existing methods that do require intensive DNN parameter tuning. It also efficiently detects test instances with the trigger, which may help to catch adversarial entities.