mtybilly/Instruct2DS huggingface dataset:
What Do Deep Saliency Models Learn about Visual Attention?
Authors: Shi Chen, Ming Jiang, Qi Zhao
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provides principled interpretation and quantification of their contributions to saliency prediction. Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes and reformulates saliency prediction as a weighted combination of probability maps connecting the bases and saliency. By applying our framework, we conduct extensive analyses from various perspectives, including the positive and negative weights of semantics, the impact of training data and architectural designs, the progressive influences of fine-tuning, and common error patterns of state-of-the-art deep saliency models. Additionally, we demonstrate the effectiveness of our framework by exploring visual attention characteristics in various application scenarios, such as the atypical attention of people with autism spectrum disorder, attention to emotion-eliciting stimuli, and attention evolution over time. Our code is publicly available at \url{https://github.com/szzexpoi/saliency_analysis}.
Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
Authors: Valentino Delle Rose, Alexander Kozachinskiy, Cristobal Rojas, Mircea Petrache, Pablo Barceló
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The Weisfeiler-Lehman (WL) test is a fundamental iterative algorithm for checking the isomorphism of graphs. It has also been observed that it underlies the design of several graph neural network architectures, whose capabilities and performance can be understood in terms of the expressive power of this test. Motivated by recent developments in machine learning applications to datasets involving three-dimensional objects, we study when the WL test is {\em complete} for clouds of Euclidean points represented by complete distance graphs, i.e., when it can distinguish, up to isometry, any arbitrary such cloud. Our main result states that the $(d-1)$-dimensional WL test is complete for point clouds in $d$-dimensional Euclidean space, for any $d\ge 2$, and only three iterations of the test suffice. Our result is tight for $d = 2, 3$. We also observe that the $d$-dimensional WL test only requires one iteration to achieve completeness.
Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving
Authors: Sepidehsadat (Sepid) Hossieni, Mohammad Amin Shabani, Saghar Irandoust, Yasutaka Furukawa
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks.In the latter task, for instance, the proposed system ``PuzzleFusion'' takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements: 1) 2D Voronoi Jigsaw Dataset, a synthetic one where pieces are generated by voronoi diagram of 2D pointset; and 2) MagicPlan Dataset, a real one from a production pipeline by MagicPlan, where pieces are room layouts constructed by augmented reality App by real-estate consumers.The qualitative and quantitative evaluations demonstrate that the proposed approach outperforms the competing methods by significant margins in all three spatial puzzle tasks. We have provided code and data in https://sepidsh.github.io/puzzlefusion.
Visual Instruction Inversion: Image Editing via Image Prompting
Authors: Thao Nguyen, Yuheng Li, Utkarsh Ojha, Yong Jae Lee
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Text-conditioned image editing has emerged as a powerful tool for editing images.However, in many situations, language can be ambiguous and ineffective in describing specific image edits.When faced with such challenges, visual prompts can be a more informative and intuitive way to convey ideas.We present a method for image editing via visual prompting.Given pairs of example that represent the "before" and "after" images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images.We leverage the rich, pretrained editing capabilities of text-to-image diffusion models by inverting visual prompts into editing instructions.Our results show that with just one example pair, we can achieve competitive results compared to state-of-the-art text-conditioned image editing frameworks.
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
Authors: Jifan Zhang, Shuai Shao, Saurabh Verma, Robert Nowak
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep networks, but the empirical performance of active learning algorithms can vary dramatically across datasets and applications. It is difficult to know in advance which active learning strategy will perform well or best in a given application. To address this, we propose the first adaptive algorithm selection strategy for deep active learning. For any unlabeled dataset, our (meta) algorithm TAILOR (Thompson ActIve Learning algORithm selection) iteratively and adaptively chooses among a set of candidate active learning algorithms. TAILOR uses novel reward functions aimed at gathering class-balanced examples. Extensive experiments in multi-class and multi-label applications demonstrate TAILOR's effectiveness in achieving accuracy comparable or better than that of the best of the candidate algorithms. Our implementation of TAILOR is open-sourced at https://github.com/jifanz/TAILOR.
Federated Compositional Deep AUC Maximization
Authors: Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Federated learning has attracted increasing attention due to the promise of balancing privacy and large-scale learning; numerous approaches have been proposed. However, most existing approaches focus on problems with balanced data, and prediction performance is far from satisfactory for many real-world applications where the number of samples in different classes is highly imbalanced. To address this challenging problem, we developed a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score. In particular, we formulate the AUC maximization problem as a federated compositional minimax optimization problem, develop a local stochastic compositional gradient descent ascent with momentum algorithm, and provide bounds on the computational and communication complexities of our algorithm. To the best of our knowledge, this is the first work to achieve such favorable theoretical results. Finally, extensive experimental results confirm the efficacy of our method.
On Learning Latent Models with Multi-Instance Weak Supervision
Authors: Kaifu Wang, Efthymia Tsamoura, Dan Roth
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\sigma$ of labels associated with multiple input instances. We formulate this problem as *multi-instance Partial Label Learning (multi-instance PLL)*, which is an extension to the standard PLL problem. Our problem is met in different fields, including latent structural learning and neuro-symbolic integration. Despite the existence of many learning techniques, limited theoretical analysis has been dedicated to this problem. In this paper, we provide the first theoretical study of multi-instance PLL with possibly an unknown transition $\sigma$. Our main contributions are as follows: First, we proposed a necessary and sufficient condition for the learnability of the problem. This condition nontrivially generalizes and relaxes the existing *small ambiguity degree* in PLL literature since we allow the transition to be deterministic. Second, we derived Rademacher-style error bounds based on the top-$k$ surrogate loss that is widely used in the neuro-symbolic literature. Furthermore, we conclude with empirical experiments for learning with an unknown transition. The empirical results align with our theoretical findings; however, they also expose the issue of scalability in the weak supervision literature.
Degraded Polygons Raise Fundamental Questions of Neural Network Perception
Authors: Leonard Tang, Dan Ley
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Datasets and Benchmarks Track), 2023

Abstract: It is well-known that modern computer vision systems often exhibit behaviors misaligned with those of humans: from adversarial attacks to image corruptions, deeplearning vision models suffer in a variety of settings that humans capably handle. Inlight of these phenomena, here we introduce another, orthogonal perspective studying the human-machine vision gap. We revisit the task of recovering images underdegradation, first introduced over 30 years ago in the Recognition-by-Componentstheory of human vision. Specifically, we study the performance and behavior ofneural networks on the seemingly simple task of classifying regular polygons atvarying orders of degradation along their perimeters. To this end, we implement theAutomated Shape Recoverability Testfor rapidly generating large-scale datasetsof perimeter-degraded regular polygons, modernizing the historically manual creation of image recoverability experiments. We then investigate the capacity ofneural networks to recognize and recover such degraded shapes when initializedwith different priors. Ultimately, we find that neural networks’ behavior on thissimple task conflicts with human behavior, raising a fundamental question of therobustness and learning capabilities of modern computer vision models
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
Authors: Blake Bordelon, Cengiz Pehlevan
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Starting from a dynamical mean field theory  description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the $\mathcal{O}(1/\sqrt{\text{width}})$ fluctuations of the DMFT order parameters over random initializations of the network weights. Our results, while perturbative in width, unlike prior analyses, are non-perturbative in the strength of feature learning. In the lazy limit of network training, all kernels are random but static in time and the prediction variance has a universal form. However, in the rich, feature learning regime, the fluctuations of the kernels and predictions are dynamically coupled with a variance that can be computed self-consistently. In two layer networks, we show how feature learning can dynamically reduce the variance of the final tangent kernel and final network predictions. We also show how initialization variance can slow down online learning in wide but finite networks. In deeper networks, kernel variance can dramatically accumulate through subsequent layers at large feature learning strengths, but feature learning continues to improve the signal-to-noise ratio of the feature kernels. In discrete time, we demonstrate that large learning rate phenomena such as edge of stability effects can be well captured by infinite width dynamics and that initialization variance can decrease dynamically. For CNNs trained on CIFAR-10, we empirically find significant corrections to both the bias and variance of network dynamics due to finite width.
TART: A plug-and-play Transformer module for task-agnostic reasoning
Authors: Kush Bhatia, Avanika Narayan, Christopher M De Sa, Christopher Ré
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional adaptation approaches, such as fine-tuning, modify the underlying models for each specific task. In-context learning, however, consistently underperforms task-specific tuning approaches even when presented with the same examples. While most existing approaches (e.g., prompt engineering) focus on the LLM's learned representations to patch this performance gap, our experiments actually reveal that LLM representations contain sufficient information to make good predictions. As such, we focus on the LLM's reasoning abilities and demonstrate that this performance gap exists due to their inability to perform simple probabilistic reasoning tasks. This raises an intriguing question: Are LLMs actually capable of learning how to reason in a task-agnostic manner? We answer this in the affirmative and, as a proof of concept, propose TART which generically improves an LLM's reasoning abilities using a synthetically trained reasoning module. TART trains this Transformer-based reasoning module in a task-agnostic manner using only synthetic logistic regression tasks and composes it with an arbitrary real-world pre-trained model without any additional training. With a single inference module, TART improves performance across different model families (GPT-Neo, Pythia, Bloom), model sizes (100M - 6B), tasks (14 NLP classification tasks), and even across different modalities (audio and vision). On the RAFT Benchmark, TART improves GPT-Neo (125M)'s performance such that it outperforms Bloom (176B), and is within $4$% of GPT-3.
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Authors: Carsten Lüth, Till Bungert, Lukas Klein, Paul Jaeger
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Active Learning (AL) aims to reduce the labeling burden by interactively selecting the most informative samples from a pool of unlabeled data. While there has been extensive research on improving AL query methods in recent years, some studies have questioned the effectiveness of AL compared to emerging paradigms such as semi-supervised (Semi-SL) and self-supervised learning (Self-SL), or a simple optimization of classifier configurations. Thus, today’s AL literature presents an inconsistent and contradictory landscape, leaving practitioners uncertain about whether and how to use AL in their tasks. In this work, we make the case that this inconsistency arises from a lack of systematic and realistic evaluation of AL methods. Specifically, we identify five key pitfalls in the current literature that reflect the delicate considerations required for AL evaluation. Further, we present an evaluation framework that overcomes these pitfalls and thus enables meaningful statements about the performance of AL methods. To demonstrate the relevance of our protocol, we present a large-scale empirical study and benchmark for image classification spanning various data sets, query methods, AL settings, and training paradigms. Our findings clarify the inconsistent picture in the literature and enable us to give hands-on recommendations for practitioners. The benchmark is hosted at https://github.com/IML-DKFZ/realistic-al.
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Authors: Fabian Paischer, Thomas Adler, Markus Hofmarcher, Sepp Hochreiter
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive success stories in mastering partially observable environments, mostly in the realm of computer games like Dota 2, StarCraft II, or MineCraft. However, existing methods lack interpretability in the sense that it is not comprehensible for humans what the agent stores in its memory.In this regard, we propose a novel memory mechanism that represents past events in human language.Our method uses CLIP to associate visual inputs with language tokens. Then we feed these tokens to a pretrained language model that serves the agent as memory and provides it with a coherent and human-readable representation of the past.We train our memory mechanism on a set of partially observable environments and find that it excels on tasks that require a memory component, while mostly attaining performance on-par with strong baselines on tasks that do not. On a challenging continuous recognition task, where memorizing the past is crucial, our memory mechanism converges two orders of magnitude faster than prior methods.Since our memory mechanism is human-readable, we can peek at an agent's memory and check whether crucial pieces of information have been stored.This significantly enhances troubleshooting and paves the way toward more interpretable agents.
Empowering Convolutional Neural Nets with MetaSin Activation
Authors: Farnood Salehi, Tunç Aydin, André Gaillard, Guglielmo Camporese, Yuxuan Wang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: ReLU networks have remained the default choice for models in the area of image prediction despite their well-established spectral bias towards learning low frequencies faster, and consequently their difficulty of reproducing high frequency visual details. As an alternative, sin networks showed promising results in learning implicit representations of visual data. However training these networks in practically relevant settings proved to be difficult, requiring careful initialization, dealing with issues due to inconsistent gradients, and a degeneracy in local minima. In this work, we instead propose replacing a baseline network’s existing activations with a novel ensemble function with trainable parameters. The proposed MetaSin activation can be trained reliably without requiring intricate initialization schemes, and results in consistently lower test loss compared to alternatives. We demonstrate our method in the areas of Monte-Carlo denoising and image resampling where we set new state-of-the-art through a knowledge distillation based training procedure. We present ablations on hyper-parameter settings, comparisons with alternative activation function formulations, and discuss the use of our method in other domains, such as image classification.
When Does Confidence-Based Cascade Deferral Suffice?
Authors: Wittawat Jitkrittum, Neha Gupta, Aditya K Menon, Harikrishna Narasimhan, Ankit Rawat, Sanjiv Kumar
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn.  A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction.  One simple deferral rule employs the confidence of the current classifier, e.g., based on the maximum predicted softmax probability.  Despite being oblivious to the structure of the cascade --- e.g., not modelling the errors of downstream models --- such confidence-based deferral often works remarkably well in practice.  In this paper, we seek to better understand the conditions under which confidence-based deferral may fail, and when alternate deferral strategies can perform better.  We first present a theoretical characterisation of the optimal deferral rule, which precisely characterises settings under which confidence-based deferral may suffer.  We then study post-hoc deferral mechanisms, and demonstrate they can significantly improve upon confidence-based deferral in settings where (i)  downstream models are specialists that only work well on a subset of inputs, (ii) samples are subject to label noise, and (iii) there is distribution shift between the train and test set.
DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation
Authors: Yiqun Duan, Jinzhao Zhou, Zhen Wang, Yu-Kai Wang, Chin-teng Lin
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34\%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without the need for word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively.
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
Authors: BANG AN, Xun Zhou, YONGJIAN ZHONG, Tianbao Yang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The problem of urban event ranking aims at predicting the top-$k$ most risky locations of future events such as traffic accidents and crimes. This problem is of fundamental importance to public safety and urban administration especially when limited resources are available. The problem is, however, challenging due to complex and dynamic spatio-temporal correlations between locations, uneven distribution of urban events in space, and the difficulty to correctly rank nearby locations with similar features. Prior works on event forecasting mostly aim at accurately predicting the actual risk score or counts of events for all the locations. Rankings obtained as such usually have low quality due to prediction errors. Learning-to-rank methods directly optimize measures such as Normalized Discounted Cumulative Gain (NDCG), but cannot handle the spatiotemporal autocorrelation existing among locations. Due to the common assumption that items are independent. In this paper, we bridge the gap by proposing a novel spatial event ranking approach named SpatialRank. SpatialRank features  adaptive graph convolution layers that dynamically learn the spatiotemporal dependencies across locations from data. In addition, the model optimizes through surrogates a hybrid NDCG loss with a spatial component to better rank neighboring spatial locations. We design an importance-sampling with a spatial filtering algorithm to effectively evaluate the loss during training. Comprehensive experiments on three real-world datasets demonstrate that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-art methods in terms of NDCG by up to 12.7%.
An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions
Authors: Mohammad Jalali, Cheuk Ting Li, Farzan Farnia
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The evaluation of generative models has received significant attention in the machine learning community.  When applied to a multi-modal distribution which is common among image datasets, an intuitive evaluation criterion is the number of modes captured by the generative model. While several scores have been proposed to evaluate the quality and diversity of a model's generated data, the correspondence between existing scores and the number of modes in the distribution is unclear. In this work, we propose an information-theoretic diversity evaluation method for multi-modal underlying distributions. We utilize the R\'enyi Kernel Entropy (RKE) as an evaluation score based on quantum information theory to measure the number of modes in generated samples. To interpret the proposed evaluation method, we show that the RKE score can output the number of modes of a mixture of sub-Gaussian components. We also prove estimation error bounds for estimating the RKE score from limited data, suggesting a fast convergence of the empirical RKE score to the score for the underlying data distribution. Utilizing the RKE score, we conduct an extensive evaluation of state-of-the-art generative models over standard image datasets. The numerical results indicate that while the recent algorithms for training generative models manage to improve the mode-based diversity over the earlier architectures, they remain incapable of capturing the full diversity of real data. Our empirical results provide a ranking of widely-used generative models based on the RKE score of their generated samples.
A Cross-Moment Approach for Causal Effect Estimation
Authors: Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We consider the problem of estimating the causal effect of a treatment on an outcome in  linear structural causal models (SCM) with latent confounders when we have access to a single proxy variable.Several methods (such as difference-in-difference (DiD) estimator or negative outcome control) have been proposed in this setting in the literature. However, these approaches require either restrictive assumptions on the data generating model or having access to at least two proxy variables.We propose a method to estimate the causal effect using cross moments between the treatment, the outcome, and the proxy variable. In particular, we show that the causal effect can be identified with simple arithmetic operations on the cross moments if the latent confounder in linear SCM is non-Gaussian.In this setting, DiD estimator provides an unbiased estimate only in the special case where the latent confounder has exactly the same direct causal effects on the outcomes in the pre-treatment and post-treatment phases. This translates to the common trend assumption in DiD, which we effectively relax.Additionally, we provide an impossibility result that shows the causal effect cannot be identified if the observational distribution over the treatment, the outcome, and the proxy is jointly Gaussian. Our experiments on both synthetic and real-world datasets showcase the effectivenessof the proposed approach in estimating the causal effect.
Combining Behaviors with the Successor Features Keyboard
Authors: Wilka Carvalho Carvalho, Andre Saraiva, Angelos Filos, Andrew Lampinen, Loic Matthey, Richard L Lewis, Honglak Lee, Satinder P. Singh, Danilo Jimenez Rezende, Daniel Zoran
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The Option Keyboard (OK) was recently proposed as a method for transferring behavioral knowledge across tasks. OK transfers knowledge by adaptively combining subsets of known behaviors using Successor Features (SFs) and Generalized Policy Improvement (GPI).However, it relies on hand-designed state-features and task encodings which are cumbersome to design for every new environment.In this work, we propose the "Successor Features Keyboard" (SFK), which enables transfer with discovered state-features and task encodings.To enable discovery, we propose the "Categorical Successor Feature Approximator" (CSFA), a novel learning algorithm for estimating SFs while jointly discovering state-features and task encodings.With SFK and CSFA, we achieve the first demonstration of transfer with SFs in a challenging 3D environment where all the necessary representations are discovered.We first compare CSFA against other methods for approximating SFs and show that only CSFA discovers representations compatible with SF&GPI at this scale.We then compare SFK against transfer learning baselines and show that it transfers most quickly to long-horizon tasks.
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Authors: Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David Clifton, S. Kevin Zhou, Lawrence Staib, James Duncan
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical features and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose $\texttt{ARCO}$, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building $\texttt{ARCO}$ through the concept of variance-reduced estimation, and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing such challenging safety-critical tasks.
Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks
Authors: Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang, Cheng Chen, Min Zhang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Formally verifying deep reinforcement learning (DRL) systems suffers from both inaccurate verification results and limited scalability. The major obstacle lies in the large overestimation introduced inherently during training and then transforming the inexplicable decision-making models, i.e., deep neural networks (DNNs), into easy-to-verify models. In this paper, we propose an inverse transform-then-train approach, which first encodes a DNN into an equivalent set of efficiently and tightly verifiable linear control policies and then optimizes them via reinforcement learning.  We accompany our inverse approach with a novel neural network model called piece-wise linear decision neural networks (PLDNNs), which are compatible with most existing DRL training algorithms with comparable performance against conventional DNNs. Our extensive experiments show that, compared to DNN-based  DRL systems, PLDNN-based systems can be more efficiently and tightly verified with up to $438$ times speedup and a significant reduction in overestimation.  In particular, even a complex $12$-dimensional DRL system is efficiently verified with up to 7 times deeper computation steps.
Star-Shaped Denoising Diffusion Probabilistic Models
Authors: Andrey Okhotin, Dmitry Molchanov, Arkhipkin Vladimir, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry P Vetrov
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Denoising Diffusion Probabilistic Models (DDPMs) provide the foundation for the recent breakthroughs in generative modeling.Their Markovian structure makes it difficult to define DDPMs with distributions other than Gaussian or discrete.In this paper, we introduce Star-Shaped DDPM (SS-DDPM).Its star-shaped diffusion process allows us to bypass the need to define the transition probabilities or compute posteriors.We establish duality between star-shaped and specific Markovian diffusions for the exponential family of distributions and derive efficient algorithms for training and sampling from SS-DDPMs.In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM.However, SS-DDPMs provide a simple recipe for designing diffusion models with distributions such as Beta, von Mises–Fisher, Dirichlet, Wishart and others, which can be especially useful when data lies on a constrained manifold.We evaluate the model in different settings and find it competitive even on image data, where Beta SS-DDPM achieves results comparable to a Gaussian DDPM.Our implementation is available at https://github.com/andrey-okhotin/star-shaped
An Adaptive Algorithm for Learning with Unknown Distribution Drift
Authors: Alessio Mazzetto, Eli Upfal
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. Instead, the algorithm adapts to the sample data. Without explicitly estimating the drift, the algorithm learns a family of functions with almost the same error as a learning algorithm that knows the magnitude of the drift in advance. Furthermore, since our algorithm adapts to the data, it can guarantee a better learning error than an algorithm that relies on loose bounds on the drift. We demonstrate the application of our technique in two fundamental learning scenarios: binary classification and linear regression.
QLoRA: Efficient Finetuning of Quantized LLMs
Authors: Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information-theoretically optimal for normally distributed weights (b) Double Quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) Paged Optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small, high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations, showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training.
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
Authors: Ping Guo, Xiangpeng Wei, Yue Hu, Baosong Yang, Dayiheng Liu, Fei Huang, jun xie
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Expressing universal semantics common to all languages is helpful to understand the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic ``universals'' for any two languages. In this paper, we propose Emma-X: an EM-like Multilingual pre-training Algorithm, to learn Cross-lingual universals with the aid of excessive multilingual non-parallel data. Emma-X unifies the cross-lingual representation learning task and an extra semantic relation prediction task within an EM framework. Both the extra semantic classifier and the cross-lingual sentence encoder approximate the semantic relation of two sentences, and supervise each other until convergence. To evaluate Emma-X, we conduct experiments on xrete, a newly introduced benchmark containing 12 widely studied cross-lingual tasks that fully depend on sentence-level representations. Results reveal that Emma-X achieves state-of-the-art performance. Further geometric analysis of the built representation space with three requirements demonstrates the superiority of Emma-X over advanced models.
Tester-Learners for Halfspaces: Universal Algorithms
Authors: Aravind Gollakota, Adam Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We give the first tester-learner for halfspaces that succeeds universally over a wide class of structured distributions. Our universal tester-learner runs in fully polynomial time and has the following guarantee: the learner achieves error $O(\mathrm{opt}) + \epsilon$ on any labeled distribution that the tester accepts, and moreover, the tester accepts whenever the marginal is any distribution that satisfies a Poincare inequality. In contrast to prior work on testable learning, our tester is not tailored to any single target distribution but rather succeeds for an entire target class of distributions. The class of Poincare distributions includes all strongly log-concave distributions, and, assuming the Kannan--Lovasz--Simonovits (KLS) conjecture, includes all log-concave distributions. In the special case where the label noise is known to be Massart, our tester-learner achieves error $\mathrm{opt} + \epsilon$ while accepting all log-concave distributions unconditionally (without assuming KLS).Our tests rely on checking hypercontractivity of the unknown distribution using a sum-of-squares (SOS) program, and crucially make use of the fact that Poincare distributions are certifiably hypercontractive in the SOS framework.
Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale
Authors: Zhiwei Hao, Jianyuan Guo, Kai Han, Han Hu, Chang Xu, Yunhe Wang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rationality of designing knowledge distillation (KD) approaches for limited-capacity architectures solely based on small-scale datasets is now deemed imperative. In this paper, we identify the small data pitfall that presents in previous KD methods, which results in the underestimation of the power of vanilla KD framework on large-scale datasets such as ImageNet-1K. Specifically, we show that employing stronger data augmentation techniques and using larger datasets can directly decrease the gap between vanilla KD and other meticulously designed KD variants. This highlights the necessity of designing and evaluating KD approaches in the context of practical scenarios, casting off the limitations of small-scale datasets. Our investigation of the vanilla KD and its variants in more complex schemes, including stronger training strategies and different model capacities, demonstrates that vanilla KD is elegantly simple but astonishingly effective in large-scale scenarios. Without bells and whistles, we obtain state-of-the-art ResNet-50, ViT-S, and ConvNeXtV2-T models for ImageNet, which achieve 83.1%, 84.3%, and 85.0% top-1 accuracy, respectively. PyTorch code and checkpoints can be found at https://github.com/Hao840/vanillaKD.
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context
Authors: Julian Tanke, Oh-Hun Kwon, Felix B Mueller, Andreas Doering, Jürgen Gall
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Datasets and Benchmarks Track), 2023

Abstract: Forecasting human motion of multiple persons is very challenging. It requires to model the interactions between humans and the interactions with objects and the environment. For example, a person might want to make a coffee, but if the coffee machine is already occupied the person will haveto wait. These complex relations between scene geometry and persons ariseconstantly in our daily lives, and models that wish to accurately forecasthuman behavior will have to take them into consideration. To facilitate research in this direction, we propose Humans in Kitchens, alarge-scale multi-person human motion dataset with annotated 3D human poses, scene geometry and activities per person and frame.Our dataset consists of over 7.3h recorded data of up to 16 persons at the same time in four kitchen scenes, with more than 4M annotated human poses, represented by a parametric 3D body model. In addition, dynamic scene geometry and objects like chair or cupboard are annotated per frame. As first benchmarks, we propose two protocols for short-term and long-term human motion forecasting.
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
Authors: Ningyi Liao, Siqiang Luo, Xiang Li, Jieming Shi
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Heterophilous Graph Neural Network (GNN) is a family of GNNs that specializes in learning graphs under heterophily, where connected nodes tend to have different labels. Most existing heterophilous models incorporate iterative non-local computations to capture node relationships. However, these approaches have limited application to large-scale graphs due to their high computational costs and challenges in adopting minibatch schemes. In this work, we study the scalability issues of heterophilous GNN and propose a scalable model, LD2, which simplifies the learning process by decoupling graph propagation and generating expressive embeddings prior to training. Theoretical analysis demonstrates that LD2 achieves optimal time complexity in training, as well as a memory footprint that remains independent of the graph scale. We conduct extensive experiments to showcase that our model is capable of lightweight minibatch training on large-scale heterophilous graphs, with up to $15\times$ speed improvement and efficient memory utilization, while maintaining comparable or better performance than the baselines.
On Single-Index Models beyond Gaussian Data
Authors: Aaron Zweig, Loucas PILLAUD-VIVIEN, Joan Bruna
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Sparse high-dimensional functions have arisen as a rich framework to study the behavior of gradient-descent methods using shallow neural networks, and showcasing its ability to perform feature learning beyond linear models. Amongst those functions, the simplest are single-index models $f(x) = \phi( x \cdot \theta^*)$, where the labels are generated by an arbitrary non-linear link function $\phi$ of an unknown one-dimensional projection $\theta^*$ of the input data. By focusing on Gaussian data, several recent works have built a remarkable picture, where the so-called information exponent (related to the regularity of the link function) controls the required sample complexity. In essence, these tools exploit the stability and spherical symmetry of Gaussian distributions.In this work, we explore extensions of this picture beyond the Gaussian setting, where both stability or symmetry might be violated. Focusing on the planted setting where $\phi$ is known, our main results establish that Stochastic Gradient Descent recovers the unknown direction $\theta^*$ with constant probability in the high-dimensional regime, under mild assumptions that significantly extend ~[Yehudai and Shamir,20].