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d259108957 | Distributed and federated learning algorithms and techniques associated primarily with minimization problems. However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is becoming more apparent. In this paper, we provide a unified convergence analysis of communication-efficient local training methods for distributed variational inequality problems (VIPs). Our approach is based on a general key assumption on the stochastic estimates that allows us to propose and analyze several novel local training algorithms under a single framework for solving a class of structured non-monotone VIPs. We present the first local gradient descent-accent algorithms with provable improved communication complexity for solving distributed variational inequalities on heterogeneous data. The general algorithmic framework recovers state-of-the-art algorithms and their sharp convergence guarantees when the setting is specialized to minimization or minimax optimization problems. Finally, we demonstrate the strong performance of the proposed algorithms compared to state-of-the-art methods when solving federated minimax optimization problems. * Equal contribution. Parts of this work were presented at 1 "Acceleration" = whether the algorithm enjoys acceleration in communication compared to its non-local counterpart, "Variance Red.?" = whether the algorithm (in stochastic setting) applies variance reduction, "Communication Complexity" = the communication complexity of the algorithm. 2 Here κ = L/µ denotes the condition number where L is the Lipschitz parameter and µ is the modulus of strong convexity, σ 2 * corresponds to the variance level at the optimum (σ 2 * = 0 in deterministic setting), and ∆ captures the bounded variance. For a more detailed comparison of complexities, please also refer to Table 2. Constantinos Daskalakis, Stratis Skoulakis, and Manolis Zampetakis. The complexity of constrained min-max optimization. In . SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. In NeurIPS, 2014. (Cited on A) Yuyang Deng and Mehrdad Mahdavi. Local stochastic gradient descent ascent: Convergence analysis and communication efficiency. . Next: In-network nonconvex optimization. IEEE Transactions on Signal and Information Processing over Networks, 2(2):120-136, 2016. (Cited on A) Jelena Diakonikolas, Constantinos Daskalakis, and Michael I Jordan. Efficient methods for structured nonconvexnonconcave min-max optimization. In AISTATS, 2021. (Cited on A)Laurent El Ghaoui and Hervé Lebret. Robust solutions to least-squares problems with uncertain data. SIAM | Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates |
d7289949 | Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where highdimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier. * Equal contribution. | DIET NETWORKS: THIN PARAMETERS FOR FAT GENOMICS |
d229212743 | Over the past years, deep generative models have achieved a new level of performance.Generated data has become difficult, if not impossible, to be distinguished from real data.While there are plenty of use cases that benefit from this technology, there are also strong concerns on how this new technology can be misused to generate deep fakes and enable misinformation at scale.Unfortunately, current deep fake detection methods are not sustainable, as the gap between real and fake continues to close.In contrast, our work enables a responsible disclosure of such state-of-the-art generative models, that allows model inventors to fingerprint their models, so that the generated samples containing a fingerprint can be accurately detected and attributed to a source.Our technique achieves this by an efficient and scalable ad-hoc generation of a large population of models with distinct fingerprints.Our recommended operation point uses a 128-bit fingerprint which in principle results in more than 10 38 identifiable models.Experiments show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution.Code and models are available at GitHub. | RESPONSIBLE DISCLOSURE OF GENERATIVE MODELS USING SCALABLE FINGERPRINTING |
d257255328 | Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, "floaters" and other artifacts on street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views. We also use the the noisy and sparse LiDAR points to boost the training and learn a robust geometry and reprojection based confidence to address the depth outliers. Moreover, we extend our S-NeRF for reconstructing moving vehicles that is impracticable for conventional NeRFs. Thorough experiments on the large-scale driving datasets (e.g., nuScenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7∼ 40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering. * Equal contribution | S-NERF: NEURAL RADIANCE FIELDS FOR STREET VIEWS |
d256615829 | Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones. However, the core limitation of previous detection methods is being incapable of giving correct predictions on adversarial sentences unlike defense methods from other paradigms. To solve this issue, this paper proposes TextShield: (1) we discover a link between text attack and saliency information, and then we propose a saliency-based detector, which can effectively detect whether an input sentence is adversarial or not.(2)We design a saliencybased corrector, which converts the detected adversary sentences to benign ones. By combining the saliency-based detector and corrector, TextShield extends the detection-only paradigm to a detection-correction paradigm, thus filling the gap in the existing detection-based defense. Comprehensive experiments show that (a) TextShield consistently achieves higher or comparable performance than state-ofthe-art defense methods across various attacks on different benchmarks. (b) our saliency-based detector outperforms existing detectors for detecting adversarial sentences. * This work is done during Lingfeng's internship at Tencent AI Lab, †refers to the corresponding authors. 1 Through the rest of the paper, text attack specifically refers to word-level text attacks.Published as a conference paper at ICLR 2023 use the corrected adversarial sentence for the model prediction. Therefore, considering the paradigm problem, we propose a detection-correction defense method in this paper. | TEXTSHIELD: BEYOND SUCCESSFULLY DETECTING ADVERSARIAL SENTENCES IN TEXT CLASSIFICATION |
d263605591 | Amazon products Input Views Novel Views Input Views Novel Views Input Views Novel ViewsFigure 1: LEAP performs 3D modeling from sparse views without camera pose information.We show the capability of LEAP on real-world cases with three unposed image inputs.We show one of the inputs. | LEAP: LIBERATE SPARSE-VIEW 3D MODELING FROM CAMERA POSES |
d260334705 | Can we better anticipate an actor's future actions (e.g.mix eggs) by knowing what commonly happens after the current action (e.g.crack eggs)?What if the actor also shares the goal (e.g.make fried rice) with us?The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction.We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal.We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g.recipes, how-tos), have the potential to help LTA from both perspectives.It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively.We propose AntGPT, which represents video observations as sequences of human actions, and uses the action representation for an LLM to infer the goals and model temporal dynamics.AntGPT achieves stateof-the-art performance on Ego4D LTA v1 and v2, EPIC-Kitchens-55, as well as EGTEA GAZE+, thanks to LLMs' goal inference and temporal dynamics modeling capabilities.We further demonstrate that these capabilities can be effectively distilled into a compact neural network 1.3% of the original LLM model size.Code and model will be released at brown-palm.github.io/AntGPT. | AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? |
d219965949 | In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Due to its discrete nature, they can be combined in a straightforward manner with entropy coding schemes for lossless compression without the need for bits-back coding. We discuss the potential difference in flexibility between invertible flows for discrete random variables and flows for continuous random variables and show that (integer) discrete flows are more flexible than previously claimed. We furthermore investigate the influence of quantization operators on optimization and gradient bias in integer discrete flows. Finally, we introduce modifications to the architecture to improve the performance of this model class for lossless compression. | IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression |
d57375714 | Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve. We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator. Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed. The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes. The global pathway focuses on the image background and the general image layout. We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations. We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background. | GENERATING MULTIPLE OBJECTS AT SPATIALLY DISTINCT LOCATIONS |
d233481716 | Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles. This task is challenging, as coarse annotations (tags, boxes) lack precise pixel localization whereas sparse annotations (points, scribbles) lack broad region coverage. Existing methods tackle these two types of weak supervision differently: Class activation maps are used to localize coarse labels and iteratively refine the segmentation model, whereas conditional random fields are used to propagate sparse labels to the entire image. We formulate weakly supervised segmentation as a semi-supervised metric learning problem, where pixels of the same (different) semantics need to be mapped to the same (distinctive) features. We propose 4 types of contrastive relationships between pixels and segments in the feature space, capturing low-level image similarity, semantic annotation, co-occurrence, and feature affinity. They act as priors; the pixel-wise feature can be learned from training images with any partial annotations in a data-driven fashion. In particular, unlabeled pixels in training images participate not only in data-driven grouping within each image, but also in discriminative feature learning within and across images. We deliver a universal weakly supervised segmenter with significant gains on Pascal VOC and DensePose. Our code is publicly available at https://github.com/twke18/SPML. arXiv:2105.00957v2 [cs.CV] 11 May 2021 Published as a conference paper at ICLR 2021 image image tags bounding boxes labeled points scribbles SOTA methods CAM + refine box-wise CAM CRF loss CRF loss our method single pixel-to-segment contrastive learning loss formulation our relative gain +8.6% +4.7% +24.7% +1.4%Figure 2: We propose a unified framework for weakly supervised semantic segmentation with different types of annotations. We demonstrate consistent performance gains compared to the state-ofthe-art (SOTA) methods: Chang et al. (2020) for image tags, Song et al. (2019) for bounding boxes, and Tang et al. (2018b) for points and scribbles. For tags and boxes, Class Activation Maps (CAM) (Zhou et al., 2016) are often used to localize semantics as an initial mask and iteratively refine the segmentation model, whereas for labeled points and scribbles, Conditional Random Fields (CRF) are used to propagate semantic labels to unlabeled regions based on low-level image similarity.by individual body parts, even though the ground-truth segmentation is not known during training. This task is challenging, as not only a single body part could contain several visually distinctive areas (e.g., head consists of eyes, nose, mouth, beard), but two adjacent body parts could also have the same visual appearance (e.g., upper arm, lower arm, and hand have the same skin appearance). Once the segmenter is learned, it can be applied to a test image without any annotations. | UNIVERSAL WEAKLY SUPERVISED SEGMENTATION BY PIXEL-TO-SEGMENT CONTRASTIVE LEARNING |
d12200521 | We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find encouraging results: zoneout gives significant performance improvements across tasks, yielding state-ofthe-art results in character-level language modeling on the Penn Treebank dataset and competitive results on word-level Penn Treebank and permuted sequential MNIST classification tasks.1 This is the case both when training with targets at every time-step, e.g. as in language modelling, or with a single target at the end of a variable-length sequence, e.g. as in sentiment analysis. | Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations |
d249954054 | Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning on curated datasets, or via reinforcement learning. In this work, we propose a novel offline RL method, implicit language Q-learning (ILQL), designed for use on language models, that combines both the flexible utility maximization framework of RL algorithms with the ability of supervised learning to leverage previously collected data, as well as its simplicity and stability. Our method employs a combination of value conservatism alongside an implicit dataset support constraint in learning value functions, which are then used to guide language model generations towards maximizing user-specified utility functions. In addition to empirically validating ILQL, we present a detailed empirical analysis of situations where offline RL can be useful in natural language generation settings, demonstrating how it can be a more effective utility optimizer than prior approaches for end-to-end dialogue, and how it can effectively optimize high variance reward functions based on subjective judgement, such as whether to label a comment as toxic or not 1 . | OFFLINE RL FOR NATURAL LANGUAGE GENERATION WITH IMPLICIT LANGUAGE Q LEARNING |
d11480374 | This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "history of word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it identifies an attention scoring function that better utilizes the "history of word" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of Fusion-Net with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%. A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426, 2017.In this appendix, we compare with published state-of-the-art architectures on the SQuAD dev set. The comparison is shown inFigure 5and 6 for EM and F1 score respectively. The performance of FusionNet is shown under different training epochs. Each epoch loops through all the examples in the training set once. On a single NVIDIA GeForce GTX Titan X GPU, each epoch took roughly 20 minutes when batch size 32 is used. | FUSIONNET: FUSING VIA FULLY-AWARE ATTENTION WITH APPLICATION TO MACHINE COMPREHENSION |
d16367617 | Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the Generative Multi-Adversarial Network (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GMAN can be reliably trained with the original, untampered objective. We explore a number of design perspectives with the discriminator role ranging from formidable adversary to forgiving teacher. Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric. | GENERATIVE MULTI-ADVERSARIAL NETWORKS |
d9651443 | We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing. | SONG FROM PI: A MUSICALLY PLAUSIBLE NETWORK FOR POP MUSIC GENERATION |
d851777 | We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations-random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.Published as a conference paper at ICLR 2017 Emily L Denton, Soumith Chintala, Rob Fergus, et al. Deep generative image models using a Laplacian pyramid of adversarial networks. In Neural Information Processing Systems, 2015. | DEEP PROBABILISTIC PROGRAMMING |
d247158860 | Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by revisiting the surrogate loss learning, where a deep neural network is employed to approximate the evaluation metrics. Instead of pursuing an exact recovery of the evaluation metric through a deep neural network, we are reminded of the purpose of the existence of these evaluation metrics, which is to distinguish whether one model is better or worse than another. In this paper, we show that directly maintaining the relation of models between surrogate losses and metrics suffices, and propose a rank correlation-based optimization method to maximize this relation and learn surrogate losses. Compared to previous works, our method is much easier to optimize and enjoys significant efficiency and performance gains. Extensive experiments show that our method achieves improvements on various tasks including image classification and neural machine translation, and even outperforms state-of-theart methods on human pose estimation and machine reading comprehension tasks. | RELATIONAL SURROGATE LOSS LEARNING |
d53576131 | Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers.In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such an intriguing phenomenon, we propose a concept alignment method based on how units respond to the replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language. | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNS |
d248721740 | We address the task of view synthesis, generating novel views of a scene given a set of images as input. In many recent works such as NeRF (Mildenhall et al., 2020), the scene geometry is parameterized using neural implicit representations (i.e., MLPs). Implicit neural representations have achieved impressive visual quality but have drawbacks in computational efficiency. In this work, we propose a new approach that performs view synthesis using point clouds. It is the first pointbased method that achieves better visual quality than NeRF while being 100× faster in rendering speed. Our approach builds on existing works on differentiable point-based rendering but introduces a novel technique we call "Sculpted Neural Points (SNP)", which significantly improves the robustness to errors and holes in the reconstructed point cloud. We further propose to use view-dependent point features based on spherical harmonics to capture non-Lambertian surfaces, and new designs in the point-based rendering pipeline that further boost the performance. Finally, we show that our system supports fine-grained scene editing. Code is available at https://github.com/princeton-vl/SNP. | VIEW SYNTHESIS WITH SCULPTED NEURAL POINTS |
d222208920 | Autoregressive language models pretrained on large corpora have been successful at solving downstream tasks, even with zero-shot usage. However, there is little theoretical justification for their success. This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classification tasks? (2) Why do features learned using language modeling help solve downstream tasks with linear classifiers? For (1), we hypothesize, and verify empirically, that classification tasks of interest can be reformulated as next word prediction tasks, thus making language modeling a meaningful pretraining task. For (2), we analyze properties of the cross-entropy objective to show that -optimal language models in cross-entropy (log-perplexity) learn features that are O( √ )-good on natural linear classification tasks, thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks. We perform experiments to verify assumptions and validate theoretical results. Our theoretical insights motivate a simple alternative to the cross-entropy objective that performs well on some linear classification tasks. | A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks |
d258180514 | We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust linear algebraic algorithms when the updates to the input are sparse but given by an adversary with access to a query oracle. We also study robust algorithms in the standard centralized setting, where an adversary queries an algorithm in an adaptive manner, but the number of interactions between the adversary and the algorithm is bounded. We first recall a unified framework of [HKM + 20, BKM + 22, ACSS23] for answering Q adaptive queries that incurs O( √ Q) overhead in space, which is roughly a quadratic improvement over the naïve implementation, and only incurs a logarithmic overhead in query time. Although the general framework has diverse applications in machine learning and data science, such as adaptive distance estimation, kernel density estimation, linear regression, range queries, and point queries and serves as a preliminary benchmark, we demonstrate even better algorithmic improvements for (1) reducing the pre-processing time for adaptive distance estimation and (2) permitting an unlimited number of adaptive queries for kernel density estimation. Finally, we complement our theoretical results with additional empirical evaluations. | Robust Algorithms on Adaptive Inputs from Bounded Adversaries |
d219636236 | Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty. | Getting a CLUE: A Method for Explaining Uncertainty Estimates |
d248376815 | Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classification. We propose the first unified and end-toend approach for anytime dense prediction. A cascade of "exits" is attached to the model to make multiple predictions. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already sufficiently confident. Our full method, named anytime dense prediction with confidence (ADP-C), achieves the same level of final accuracy as the base model, and meanwhile significantly reduces total computation. We evaluate our method on Cityscapes semantic segmentation and MPII human pose estimation: ADP-C enables anytime inference without sacrificing accuracy while also reducing the total FLOPs of its base models by 44.4% and 59.1%. We compare with anytime inference by deep equilibrium networks and feature-based stochastic sampling, showing that ADP-C dominates both across the accuracy-computation curve. Our code is available at https://github.com/liuzhuang13/anytime. | ANYTIME DENSE PREDICTION WITH CONFIDENCE ADAPTIVITY |
d228373733 | Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountered, MPC solves an online optimization problem to choose a control action that will minimize future cost. This is a surprisingly effective strategy, but real-time performance requirements warrant the use of simple models. If the model is not sufficiently accurate, then the resulting controller can be biased, limiting performance. We present a framework for improving on MPC with model-free reinforcement learning (RL). The key insight is to view MPC as constructing a series of local Q-function approximations. We show that by using a parameter λ, similar to the trace decay parameter in TD(λ), we can systematically trade-off learned value estimates against the local Q-function approximations. We present a theoretical analysis that shows how error from inaccurate models in MPC and value function estimation in RL can be balanced. We further propose an algorithm that changes λ over time to reduce the dependence on MPC as our estimates of the value function improve, and test the efficacy our approach on challenging high-dimensional manipulation tasks with biased models in simulation. We demonstrate that our approach can obtain performance comparable with MPC with access to true dynamics even under severe model bias and is more sample efficient as compared to model-free RL. | BLENDING MPC & VALUE FUNCTION APPROXIMATION FOR EFFICIENT REINFORCEMENT LEARNING |
d210860781 | Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings.This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional.We motivate and formalize the general framework of targetembedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets-encoding the prior that variations in targets are driven by a compact set of underlying factors.As our theoretical contribution, we provide a guarantee of generalization for linear TEAs by demonstrating uniform stability, interpreting the benefit of the auxiliary reconstruction task as a form of regularization.As our empirical contribution, we extend validation of this approach beyond existing static classification applications to multivariate sequence forecasting, verifying their advantage on both linear and nonlinear recurrent architectures-thereby underscoring the further generality of this framework beyond feedforward instantiations. | TARGET-EMBEDDING AUTOENCODERS FOR SUPERVISED REPRESENTATION LEARNING |
d249953817 | The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our approach, named MaskViT, is based on two simple design decisions. First, for memory and training efficiency, we use two types of window attention: spatial and spatiotemporal. Second, during training, we mask a variable percentage of tokens instead of a fixed mask ratio. For inference, MaskViT generates all tokens via iterative refinement where we incrementally decrease the masking ratio following a mask scheduling function. On several datasets we demonstrate that MaskViT outperforms prior works in video prediction, is parameter efficient, and can generate high-resolution videos (256 × 256). Further, we demonstrate the benefits of inference speedup (up to 512×) due to iterative decoding by using MaskViT for planning on a real robot. Our work suggests that we can endow embodied agents with powerful predictive models by leveraging the general framework of masked visual modeling with minimal domain knowledge. | MaskViT: Masked Visual Pre-Training for Video Prediction |
d264172935 | We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedure, where we train (i) vision-language models to serve as both policies and value functions, and (ii) text-to-video models as dynamics models. VLP takes as input a long-horizon task instruction and current image observation, and outputs a long video plan that provides detailed multimodal (video and language) specifications that describe how to complete the final task. VLP scales with increasing computation budget where more computation time results in improved video plans, and is able to synthesize long-horizon video plans across different robotics domainsfrom multi-object rearrangement, to multi-camera bi-arm dexterous manipulation. Generated video plans can be translated into real robot actions via goal-conditioned policies, conditioned on each intermediate frame of the generated video. Experiments show that VLP substantially improves long-horizon task success rates compared to prior methods on both simulated and real robots (across 3 hardware platforms).Step 1: push blue triangle to …Step 1: push red star to left …Step 2: (re-plan) t t + k t current t goalStep 2: (re-plan)Step 2: (re-plan) | VIDEO LANGUAGE PLANNING |
d20140417 | We build on auto-encoding sequential Monte Carlo (AESMC): 1 a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions. We develop additional theoretical insights and experiment with a new training procedure which can improve both model and proposal learning. We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models. | AUTO-ENCODING SEQUENTIAL MONTE CARLO |
d221508448 | Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Multi-Time Attention Networks learn an embedding of continuous time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods. 1 1 Implementation available at : https://github.com/reml-lab/mTAN 1 arXiv:2101.10318v2 [cs.LG] 7 Jun 2021Published as a conference paper at ICLR 2021The main contributions of the mTAN model framework are: (1) It provides a flexible approach to modeling multivariate, sparse and irregularly sampled time series data (including irregularly sampled time series of partially observed vectors) by leveraging a time attention mechanism to learn temporal similarity from data instead of using fixed kernels. (2) It uses a temporally distributed latent representation to better capture local structure in time series data. (3) It provides interpolation and classification performance that is as good as current state-of-the-art methods or better, while providing significantly reduced training times.RELATED WORKAn irregularly sampled time series is a time series with irregular time intervals between observations. In the multivariate setting, there can also be a lack of alignment across different variables within the same multivariate time series. Finally, when gaps between observation times are large, the time series is also considered to be sparse. Such data occur in electronic health records (Marlin et al., astronomy (Scargle, 1982). It is well understood that such data cause significant issues for standard supervised machine learning models that typically assume fully observed, fixed-size feature representations(Marlin et al., 2012).A basic approach to dealing with irregular sampling is fixed temporal discretization. For example,Marlin et al. (2012)and Lipton et al. (2016) discretize continuous-time observations into hour-long bins. This has the advantage of simplicity, but requires ad-hoc handling of bins with more than one observation and results in missing data when bins are empty. | MULTI-TIME ATTENTION NETWORKS FOR IRREGULARLY SAMPLED TIME SERIES |
d257038864 | Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-difference (TD) learning, is known to suffer form divergence issue when the off-policy scheme is used together with linear function approximation. To overcome the divergent behavior, several off-policy TD-learning algorithms, including gradient-TD learning (GTD), and TD-learning with correction (TDC), have been developed until now. In this work, we provide a unified view of such algorithms from a purely control-theoretic perspective, and propose a new convergent algorithm. Our method relies on the backstepping technique, which is widely used in nonlinear control theory. Finally, convergence of the proposed algorithm is experimentally verified in environments where the standard TD-learning is known to be unstable. | BACKSTEPPING TEMPORAL DIFFERENCE LEARNING |
d238856821 | Existing approaches to lifelong language learning rely on plenty of labeled data for learning a new task, which is hard to obtain in most real scenarios. Considering that humans can continually learn new tasks from a handful of examples, we expect the models also to be able to generalize well on new few-shot tasks without forgetting the previous ones. In this work, we define this more challenging yet practical problem as Lifelong Few-shot Language Learning (LFLL) and propose a unified framework for it based on prompt tuning (PT) of T5. Our framework called LFPT5 takes full advantage of PT's strong few-shot learning ability, and simultaneously trains the model as a task solver and a data generator. Before learning a new domain of the same task type, LFPT5 generates pseudo (labeled) samples of previously learned domains, and later gets trained on those samples to alleviate forgetting of previous knowledge as it learns the new domain. In addition, a KL divergence loss is minimized to achieve label consistency between the previous and the current model. While adapting to a new task type, LFPT5 includes and tunes additional prompt embeddings for the new task. With extensive experiments, we demonstrate that LFPT5 can be applied to various different types of tasks and significantly outperform previous methods in different LFLL settings. | LFPT5: A UNIFIED FRAMEWORK FOR LIFELONG FEW-SHOT LANGUAGE LEARNING BASED ON PROMPT TUNING OF T5 |
d220042361 | A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the latent dynamics, and utilizing it to perform control in the latent space. Two important questions in this area are how to learn a representation that is amenable to the control problem at hand, and how to achieve an end-to-end framework for representation learning and control. In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space. We call this model control-aware representation learning (CARL). We derive a loss function for CARL that has close connection to the prediction, consistency, and curvature (PCC) principle for representation learning. We derive three implementations of CARL. In the offline implementation, we replace the locally-linear control algorithm (e.g., iLQR) used by the existing LCE methods with a RL algorithm, namely model-based soft actor-critic, and show that it results in significant improvement. In online CARL, we interleave representation learning and control, and demonstrate further gain in performance. Finally, we propose value-guided CARL, a variation in which we optimize a weighted version of the CARL loss function, where the weights depend on the TD-error of the current policy. We evaluate the proposed algorithms by extensive experiments on benchmark tasks and compare them with several LCE baselines.Preprint. Under review. | Control-Aware Representations for Model-based Reinforcement Learning |
d264590171 | Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training without the use of labels.MIM applies random crops to input images, processes them with an encoder, and then recovers the masked inputs with a decoder, which encourages the network to capture and learn structural information about objects and scenes.The intermediate feature representations obtained from MIM are suitable for fine-tuning on downstream tasks.In this paper, we propose an Image Modeling framework based on random orthogonal projection instead of binary masking as in MIM.Our proposed Random Orthogonal Projection Image Modeling (ROPIM) reduces spatially-wise token information under guaranteed bound on the noise variance and can be considered as masking entire spatial image area under locally varying masking degrees.Since ROPIM uses a random subspace for the projection that realizes the masking step, the readily available complement of the subspace can be used during unmasking to promote recovery of removed information.In this paper, we show that using random orthogonal projection leads to superior performance compared to crop-based masking.We demonstrate state-of-the-art results on several popular benchmarks. | PRE-TRAINING WITH RANDOM ORTHOGONAL PROJECTION IMAGE MODELING |
d252907467 | Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyperparameter dimensions grow, naively selected settings may lead to sub-optimal and/or unreliable results. We develop an efficient method for calibrating models such that their predictions provably satisfy multiple explicit and simultaneous statistical guarantees (e.g., upper-bounded error rates), while also optimizing any number of additional, unconstrained objectives (e.g., total run-time cost). Building on recent results in distribution-free, finite-sample risk control for general losses, we propose Pareto Testing: a two-stage process which combines multi-objective optimization with multiple hypothesis testing. The optimization stage constructs a set of promising combinations on the Pareto frontier. We then apply statistical testing to this frontier only to identify configurations that have (i) high utility with respect to our objectives, and (ii) guaranteed risk levels with respect to our constraints, with specifiable high probability. We demonstrate the effectiveness of our approach to reliably accelerate the execution of large-scale Transformer models in natural language processing (NLP) applications. In particular, we show how Pareto Testing can be used to dynamically configure multiple inter-dependent model attributes-including the number of layers computed before exiting, number of attention heads pruned, or number of text tokens considered-to simultaneously control and optimize various accuracy and cost metrics. Chilimbi. Magic pyramid: Accelerating inference with early exiting and token pruning. arXiv preprint arXiv:2111.00230, 2021. Sture Holm. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, pp. 65-70, 1979. , et al. Multi-objective hyperparameter optimization-an overview. arXiv preprint arXiv:2206.07438, 2022. . Learned token pruning for transformers. arXiv preprint arXiv:2107.00910, 2021. Joshua Knowles. Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1): 50-66, 2006. Jing Lei, James Robins, and Larry Wasserman. Distribution-free prediction sets. . Smac3: A versatile bayesian optimization package for hyperparameter optimization. Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies, pp. 142-150, 2011. | EFFICIENTLY CONTROLLING MULTIPLE RISKS WITH PARETO TESTING |
d202121359 | Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neural" policy gradient methods converge to globally optimal policies and whether they even converge at all. We answer both the questions affirmatively in the overparameterized regime. In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate. Also, we show that neural vanilla policy gradient converges sublinearly to a stationary point. Meanwhile, by relating the suboptimality of the stationary points to the representation power of neural actor and critic classes, we prove the global optimality of all stationary points under mild regularity conditions. Particularly, we show that a key to the global optimality and convergence is the "compatibility" between the actor and critic, which is ensured by sharing neural architectures and random initializations across the actor and critic. To the best of our knowledge, our analysis establishes the first global optimality and convergence guarantees for neural policy gradient methods. * equal contribution | Neural Policy Gradient Methods: Global Optimality and Rates of Convergence |
d258865836 | We present a novel approach to adapting pre-trained large language models (LLMs) to perform question answering (QA) and speech continuation.By endowing the LLM with a pre-trained speech encoder, our model becomes able to take speech inputs and generate speech outputs.The entire system is trained end-to-end and operates directly on spectrograms, simplifying our architecture.Key to our approach is a training objective that jointly supervises speech recognition, text continuation, and speech synthesis using only paired speech-text pairs, enabling a 'cross-modal' chain-of-thought within a single decoding pass.Our method surpasses existing spoken language models in speaker preservation and semantic coherence.Furthermore, the proposed model improves upon direct initialization in retaining the knowledge of the original LLM as demonstrated through spoken QA datasets.Audio samples can be found on the project website.* Equal contribution. | Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM |
d252967732 | Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in their ability to propagate and preserve long-term dependencies between nodes, i.e., they suffer from the over-squashing phenomena. This reduces their effectiveness, since predictive problems may require to capture interactions at different, and possibly large, radii in order to be effectively solved. In this work, we present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations. We give theoretical proof that our method is stable and non-dissipative, leading to two key results: long-range information between nodes is preserved, and no gradient vanishing or explosion occurs in training. We empirically validate the proposed approach on several graph benchmarks, showing that A-DGN leads to improved performance and enables to learn effectively even when dozens of layers are used.Published as a conference paper at ICLR 2023Despite the progress made in recent years in the field, many of the proposed methods suffer from the over-squashing problem (Alon & Yahav, 2021) when the number of layers increases. Specifically, when increasing the number of layers to cater for longer-range interactions, one observes an excessive amplification or an annihilation of the information being routed to the node by the message passing process to update its fixed length encoding. As such, over-squashing prevents DGNs to learn long-range information.In this work, we present Anti-Symmetric Deep Graph Network(A-DGN), a framework for effective long-term propagation of information in DGN architectures designed through the lens of ordinary differential equations (ODEs). Leveraging the connections between ODEs and deep neural architectures, we provide theoretical conditions for realizing a stable and non-dissipative ODE system on graphs through the use of anti-symmetric weight matrices. The formulation of the A-DGN layer then results from the forward Euler discretization of the achieved graph ODE. Thanks to the properties enforced on the ODE, our framework preserves the long-term dependencies between nodes as well as prevents from gradient explosion or vanishing. Interestingly, our analysis also paves the way for rethinking the formulation of standard DGNs as discrete versions of non-dissipative and stable ODEs on graphs.The key contributions of this work can be summarized as follows:• We introduce A-DGN, a novel design scheme for deep graph networks stemming from an ODE formulation. Stability and non-dissipation are the main properties that characterize our method, allowing the preservation of long-term dependencies in the information flow. • We theoretically prove that the employed ODE on graphs has stable and non-dissipative behavior. Such result leads to the absence of exploding and vanishing gradient problems during training, typical of unstable and lossy systems. • We conduct extensive experiments to demonstrate the benefits of our method. A-DGN can outperform classical DGNs over several datasets even when dozens of layers are used.The rest of this paper is organized as follows. We introduce the A-DGN framework in Section 2 by theoretically proving its properties. In Section 3, we give an overview of the related work in the field of representation learning for graphs and continuous dynamic models. Afterwards, we provide the experimental assessment of our method in Section 4. Finally, Section 5 concludes the paper. | ANTI-SYMMETRIC DGN: A STABLE ARCHITECTURE FOR DEEP GRAPH NETWORKS |
d250144675 | Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been rarely explored despite its effectiveness. Few existing augmentation recipes for video recognition naively extend the image augmentation methods by applying the same operations to the whole video frames. Our main idea is that the magnitude of augmentation operations for each frame needs to be changed over time to capture the real-world video's temporal variations. These variations should be generated as diverse as possible using fewer additional hyperparameters during training. Through this motivation, we propose a simple yet effective video data augmentation framework, DynaAugment. The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations. DynaAugment also includes an extended search space suitable for video for automatic data augmentation methods. DynaAugment experimentally demonstrates that there are additional performance rooms to be improved from static augmentations on diverse video models. Specifically, we show the effectiveness of DynaAugment on various video datasets and tasks: large-scale video recognition (Kinetics-400 and Something-Something-v2), small-scale video recognition (UCF-101 and HMDB-51), fine-grained video recognition (Diving-48 and FineGym), video action segmentation on Breakfast, video action localization on THUMOS'14, and video object detection on MOT17Det. DynaAugment also enables video models to learn more generalized representation to improve the model robustness on the corrupted videos.Preprint. Under review. | Exploring Temporally Dynamic Data Augmentation for Video Recognition |
d3331622 | Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrence matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU). KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix. It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors. Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient. Our experimental results on five standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance. These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced. | Kronecker Recurrent Units |
d232046055 | Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form. So, how should one go about finding a morphology fit for solving tasks in a given environment? Current approaches that co-adapt morphology and behavior use a specific task's reward as a signal for morphology optimization. However, this often requires expensive policy optimization and results in task-dependent morphologies that are not built to generalize. In this work, we propose a new approach, Task-Agnostic Morphology Evolution (TAME), to alleviate both of these issues. Without any task or reward specification, TAME evolves morphologies by only applying randomly sampled action primitives on a population of agents. This is accomplished using an information-theoretic objective that efficiently ranks agents by their ability to reach diverse states in the environment and the causality of their actions. Finally, we empirically demonstrate that across 2D, 3D, and manipulation environments TAME can evolve morphologies that match the multi-task performance of those learned with task supervised algorithms. Our code and videos can be found at . Quality and diversity in evolutionary modular robotics. arXiv preprint arXiv:2008.02116, 2020. 2 Tonnes F Nygaard, David Howard, and Kyrre Glette. Real world morphological evolution is feasible. arXiv preprint arXiv:2005.09288, 2020. 2Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv: | TASK-AGNOSTIC MORPHOLOGY EVOLUTION |
d51928102 | When generating adversarial examples to attack deep neural networks (DNNs), p norm of the added perturbation is usually used to measure the similarity between original image and adversarial example. However, such adversarial attacks perturbing the raw input spaces may fail to capture structural information hidden in the input. This work develops a more general attack model, i.e., the structured attack (StrAttack), which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. An ADMM (alternating direction method of multipliers)-based framework is proposed that can split the original problem into a sequence of analytically solvable subproblems and can be generalized to implement other attacking methods. Strong group sparsity is achieved in adversarial perturbations even with the same level of p -norm distortion (p ∈ {1, 2, ∞}) as the state-of-the-art attacks. We demonstrate the effectiveness of StrAttack by extensive experimental results on MNIST, CIFAR-10 and ImageNet. We also show that StrAttack provides better interpretability (i.e., better correspondence with discriminative image regions) through adversarial saliency map (Papernot et al., 2016b) and class activation map (Zhou et al., 2016). | STRUCTURED ADVERSARIAL ATTACK: TOWARDS GENERAL IMPLEMENTATION AND BETTER INTERPRETABILITY |
d257102638 | When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrained deep ImageNet classifiers that are available in popular repositories. We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training. Moreover, we find a subset of ViT models that outperform any other models in terms of uncertainty estimation performance. For example, we discovered an unprecedented 99% top-1 selective accuracy on ImageNet at 47% coverage (and 95% top-1 accuracy at 80%) for a ViT model, whereas a competing EfficientNet-V2-XL cannot obtain these accuracy constraints at any level of coverage. Our companion paper, also published in ICLR 2023(Galil et al., 2023), examines the performance of these classifiers in a class-out-of-distribution setting. | WHAT CAN WE LEARN FROM THE SELECTIVE PREDICTION AND UNCERTAINTY ESTIMATION PERFORMANCE OF 523 IMAGENET CLASSIFIERS? |
d244130249 | The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. Existing attacks mostly remain impractical due to having high false positive rates, where non-member samples are often erroneously predicted as members. This type of error makes the predicted membership signal unreliable, especially since most samples are non-members in real world applications. In this work, we argue that membership inference attacks can benefit drastically from difficulty calibration, where an attack's predicted membership score is adjusted to the difficulty of correctly classifying the target sample. We show that difficulty calibration can significantly reduce the false positive rate of a variety of existing attacks without a loss in accuracy. * Work done during an internship at Facebook. | ON THE IMPORTANCE OF DIFFICULTY CALIBRATION IN MEMBERSHIP INFERENCE ATTACKS |
d14911774 | The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communication. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message from a fixed, arbitary vocabulary to the receiver. The receiver must rely on this message to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore how to make changes to the game environment to cause the "word meanings" induced in the game to better reflect intuitive semantic properties of the images. In addition, we present a simple strategy for grounding the agents' code into natural language. Both of these are necessary steps towards developing machines that are able to communicate with humans productively. * Work done while at Facebook AI Research.The central problem of our program, then, is the following: How do we design environments that foster the development of a language that is portable to new situations and to new communication partners (in particular humans)?We start from the most basic challenge of using a language in order to refer to things in the context of a two-agent game. We focus on two questions. First, whether tabula rasa agents succeed in communication. Second, what features of the environment lead to the development of codes resembling human language.We assess this latter question in two ways. First, we consider whether the agents associate general conceptual properties, such as broad object categories (as opposed to low-level visual properties), to the symbols they learn to use. Second, we examine whether the agents' "word usage" is partially interpretable by humans in an online experiment.Other researchers have proposed communication-based environments for the development of coordination-capable AI. Work in multi-agent systems has focused on the design of pre-programmed communication systems to solve specific tasks (e.g., robot soccer, Stone & Veloso 1998). Most related to our work,Sukhbaatar et al. (2016) andFoerster et al. (2016)show that neural networks can evolve communication in the context of games without a pre-coded protocol. We pursue the same question, but further ask how we can change our environment to make the emergent language more interpretable.Others (e.g., the SHRLDU program of Winograd 1971 or the game inWang et al. 2016)propose building a communicating AI by putting humans in the loop from the very beginning. This approach has benefits but faces serious scalability issues, as active human intervention is required at each step. An attractive component of our game-based paradigm is that humans may be added as players, but do not need to be there all the time. | MULTI-AGENT COOPERATION AND THE EMERGENCE OF (NATURAL) LANGUAGE |
d228063969 | Neural networks have shown tremendous potential for reconstructing highresolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. To cope with this challenge, this paper advocates a convex duality framework that makes a two-layer fully-convolutional ReLU denoising network amenable to convex optimization. The convex dual network not only offers the optimum training with convex solvers, but also facilitates interpreting training and prediction. In particular, it implies training neural networks with weight decay regularization induces path sparsity while the prediction is piecewise linear filtering. A range of experiments with MNIST and fastMRI datasets confirm the efficacy of the dual network optimization problem. arXiv:2012.05169v1 [cs.LG] 9 Dec 2020Lovedeep Gondara. Medical image denoising using convolutional denoising autoencoders. of 3d magnetic resonance images with multi-channel residual learning of convolutional neural network. | CONVEX REGULARIZATION BEHIND NEURAL RECONSTRUCTION |
d258331993 | Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visual Planning (VP 2 ), includes simulated environments with 11 task categories and 310 task instance definitions, a full planning implementation, and training datasets containing scripted interaction trajectories for each task category. A central design goal of our benchmark is to expose a simple interface -a single forward prediction call -so it is straightforward to evaluate almost any action-conditioned video prediction model. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling by analyzing five highly-performant video prediction models, finding that while scale can improve perceptual quality when modeling visually diverse settings, other attributes such as uncertainty awareness can also aid planning performance. Additional visualizations and code are at this link.Published as a conference paper at ICLR 2023 setup except the video predictor. It includes simulated environments, specific start/goal task instance specifications, training datasets of noisy expert video interaction data, and a fully configured modelbased control algorithm. | A CONTROL-CENTRIC BENCHMARK FOR VIDEO PREDICTION |
d259075723 | Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding client-side detectability of MS attacks were raised, questioning their practicality once they are publicly known. In this work, for the first time, we thoroughly study the problem of client-side detectability. We demonstrate that most prior MS attacks, which fundamentally rely on one of two key principles, are detectable by principled client-side checks. Further, we formulate desiderata for practical MS attacks and propose SEER, a novel attack framework that satisfies all desiderata, while stealing user data from gradients of realistic networks, even for large batch sizes (up to 512 in our experiments) and under secure aggregation. The key insight of SEER is the use of a secret decoder, which is jointly trained with the shared model. Our work represents a promising first step towards more principled treatment of MS attacks, paving the way for realistic data stealing that can compromise user privacy in real-world deployments.Most prior MS attacks rely on one of two key underlying principles. One attack class[13,14,15,16]uses malicious model modifications to encourage different types of sparsity in dense layer gradients, enabling the application of analytical honest attacks-we refer to these attacks as boosted analytical.Other attacks utilize example disaggregation[17,18], reducing the effective batch size in the gradient space by restricting the gradient flow, which permits the use of optimization-based honest attacks.Client-side detectability Nearly all prior work in the field[8,13,14,15,19,17,18,20]has raised the issue of client-side detectability of MS attacks, i.e., an FL client may be able to detect malicious server activity, and decide to opt out of the current or all future rounds. Despite such concerns, no attempts have so far been made to study, quantify, or improve client-side detectability of MS attacks.This work: detecting and disguising malicious server attacks In this work, we thoroughly study the question of client-side detectability of MS attacks. We demonstrate that while boosted analytical and example disaggregation attacks pose a real threat as zero-day exploits, now that their key principles are known, all current (and future) attacks from these two classes are client-side detectable in a principled way, bringing into question their practicality. Notably, we demonstrate the detectability of example disaggregation attacks by introducing D-SNR, a novel vulnerability metric.With this in mind, we observe that such limitations of prior MS attacks arise from their fundamental reliance on the honest attacks they lift. Namely, boosted analytical attacks always require handcrafted modifications which are weight-space detectable, and example disaggregation attacks rely on the success of disaggregation, which is equally evident to any party observing the gradients, i.e., it is gradient-space detectable. This illustrates the need for fundamentally different attack approaches.As a promising first step in that direction, we propose a novel attack framework SEER, which recovers data from batch sizes up to 512, yet is by design harder to detect than prior attacks. Our key insights are that (i) gradient-space detection can be evaded using a secret decoder, disaggregating the data in a space unknown to clients, and (ii) jointly optimizing the decoder and the shared model with SGD avoids handcrafted modifications and allows for effective reconstruction. Importantly, SEER does not lift any prior honest attack and does not require restrictive assumptions such as the ability to tweak the architecture, side-channel information, or knowledge of batch normalization data or labels.Key contributions Our work makes the following contributions. | Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning |
d231934149 | Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive (in terms the Weisfeiler-Lehman graph isomorphism test) than message-passing GNNs. Augmenting GNNs with TOGL leads to improved predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data. | TOPOLOGICAL GRAPH NEURAL NETWORKS |
d238583483 | This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL procedures such as exploration and exploitation, in a sample efficient manner. We focus on the low-rank Markov Decision Processes (MDPs) where the transition dynamics correspond to a low-rank transition matrix. Unlike prior works that assume the representation is known (e.g., linear MDPs), here we need to learn the representation for the low-rank MDP. We study both the online RL and offline RL settings. For the online setting, operating with the same computational oracles used in FLAMBE(Agarwal et al., 2020b)--the state-of-art algorithm for learning representations in low-rank MDPs, we propose an algorithm REP-UCB-Upper Confidence Bound driven REPresentation learning for RL, which significantly improves the sample complexity from O(with d being the rank of the transition matrix (or dimension of the ground truth representation), A being the number of actions, and γ being the discount factor. Notably, REP-UCB is simpler than FLAMBE, as it directly balances the interplay between representation learning, exploration, and exploitation, while FLAMBE is an explorethen-commit style approach and has to perform reward-free exploration step-by-step forward in time. For the offline RL setting, we develop an algorithm that leverages pessimism to learn under a partial In this work, we study the representation learning question under the low-rank MDP assumption. Concretely, a low-rank MDP assumes that the MDP transition matrix admits a low-rank factorization, i.e., there exists two unknown mappings µ(s ), φ(s, a), such that P (s |s, a) = µ(s ) φ(s, a) for all s, a, s , where P (s |s, a) is the probability of transiting to the next state s under the current state and action (s, a). The representation φ in a low-rank MDP not only linearizes the optimal state-action value function of the MDP (Jin et al., 2020a), but also linearizes the transition operator. A low-rankness assumption on large stochastic matrices is a common and natural assumption and has enabled successful development of algorithms for real world applications such as movie recommendation systems (Koren et al., 2009). We note that a low-rank MDP strictly generalizes the linear MDP model (Yang and Wang, 2020; Jin et al., 2020a) which assumes φ is known a priori. The unknown representation φ makes learning in low-rank MDPs much more challenging than that in linear MDPs since one can no longer directly use linear function approximations. On the other hand, the fact that linear MDPs can be solved statistical and computational efficiently if φ is known a priori implies that if one could learn the representation of the low-rank MDP, one could then efficiently learn the optimal policy. Indeed, prior works have shown that learning in low-rank MDPs is statistically feasible (Jiang et al., 2017; Sun et al., 2019; Du et al., 2021) via leveraging rich function approximators. However, these algorithms are version space algorithms and are not computationally efficient. Recent work FLAMBE proposes an oracle-efficient algorithm 1 that learns in low-rank MDPs with a polynomial sample complexity, where the computation oracle is Maximum Likelihood Estimation (MLE) operating under the standard supervised learning style Empirical Risk Minimization (ERM) setting. In this work, we follow the same setup from FLAMBE (Agarwal et al., 2020b), and propose a new algorithm -Upper Confidence Bound driven Representation Learning, Exploration and Exploitation (REP-UCB), which can learn a near optimal policy for a low-rank MDP with a polynomial sample complexity and is oracle-efficient. Comparing to FLAMBE, our algorithm significantly improves the sample complexity fromwhere d is the rank of the transition matrix (or dimension of the true representation), A is the number of actions, is the suboptimality gap and γ ∈ [0, 1) is the discount factor in the MDP. Our algorithm is also arguably much simpler than FLAMBE: FLAMBE is an explorethen-commit algorithm, has to explore in a layer-by-layer forward way, and does not permit data sharing across different time steps. In contrast, REP-UCB carefully trades exploration versus exploitation by combining the reward signal and exploration bonus (constructed using the latest learned representation), and enables data sharing across all time steps. 2 Our sample complexity nearly matches the ones from those computationally inefficient algorithms (Jiang et al., 2017; Sun et al., 2019; Du et al., 2021). We summarize the comparison with the prior works that study representation learning inTable 1.In addition to the online exploration setting, we also show that our new techniques can be directly used for designing offline RL algorithms for low-rank MDPs under partial coverage. More specifically, we propose an algorithm REP-LCB-Lower Confidence Bound driven Reprepresentation Learning for offline RL, that given an offline dataset, can learn to compete against any policy (including history-dependent policies) as long as it is covered by the offline data where the coverage is measured using the relative condition number(Agarwal et al., 2021)associated with the ground truth representation. Thus, our offline RL result generalizes prior offline RL works on linear MDPs (Jin et al., 2020b; Zhang et al., 2021) which assume representation is known a priori and use linear function approximation. Computation-wise, our approach uses one call to the MLE computation oracle, and hence is oracle-efficient. REP-LCB is the first oracle efficient offline algorithm for low-rank MDP enjoying the aforementioned statistical guarantee. See Section 2 for a more detailed comparison with the existing literature on representation learning in offline RL. 1 The oracle generally refers to supervised learning style empirical risk minimization oracle. We seek to design an algorithm that runs in polynomial time with each oracle call counting as O(1). The reduction to supervised learning has lead to many successful provable and practical algorithms in contextual bandit(Agarwal et al., 2014;Dudík et al., 2017; Foster and Rakhlin, 2020) and RL (Du et al., 2019a; Misra et al., 2020). 2 Our algorithm and analysis can be easily extended to finite horizon non-stationary setting. We choose the discounted infinite horizon setting to contrast our results to FLAMBE: FLAMBE is not capable of learning stationary policies under the discounted infinite horizon setting. | Representation Learning for Online and Offline RL in Low-rank MDPs |
d53216818 | We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world. | Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control |
d257557557 | While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and accelerate the development of new breeds of soft robots, a comprehensive virtual platform -with well-established tasks, environments, and evaluation metricsis needed. In this work, we introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturallyinspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. Further, it provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control. Combined, these elements form a feature-rich platform for analysis and development of soft robot co-design algorithms. We benchmark prevalent representations and co-design algorithms, and shed light on 1) the interplay between environment, morphology, and behavior 2) the importance of design space representations 3) the ambiguity in muscle formation and controller synthesis and 4) the value of differentiable physics. We envision that SoftZoo will serve as a standard platform and template an approach toward the development of novel representations and algorithms for co-designing soft robots' behavioral and morphological intelligence. Demos are available on our project page 1 . * This work was done during an internship at the MIT-IBM Watson AI Lab. | SOFTZOO: A SOFT ROBOT CO-DESIGN BENCHMARK FOR LOCOMOTION IN DIVERSE ENVIRONMENTS |
d238857090 | Recent methods for embodied instruction following are typically trained end-toend using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46%) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49%). 1 Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions. 2 1 The official ALFRED leaderboard: https://leaderboard.allenai.org/alfred/submissions/public. 2 Project webpage with code and pre-trained models: | FILM: FOLLOWING INSTRUCTIONS IN LANGUAGE WITH MODULAR METHODS |
d247244493 | Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine "bad" labels through a "zig-zag" learning path, which occurs on both toy and real datasets. Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks. Inspired by this, we propose a new knowledge distillation scheme, Filter-KD, which improves downstream classification performance in various settings. | BETTER SUPERVISORY SIGNALS BY OBSERVING LEARNING PATHS |
d219558836 | Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a large variance of the task performance. Previous literature(Devlin et al., 2019;Lee et al., 2020;Dodge et al., 2020)identified two potential reasons for the observed instability: catastrophic forgetting and a small size of the fine-tuning datasets. In this paper, we show that both hypotheses fail to explain the fine-tuning instability. We analyze BERT, RoBERTa, and ALBERT, finetuned on three commonly used datasets from the GLUE benchmark and show that the observed instability is caused by optimization difficulties that lead to vanishing gradients. Additionally, we show that the remaining variance of the downstream task performance can be attributed to differences in generalization where fine-tuned models with the same training loss exhibit noticeably different test performance. Based on our analysis, we present a simple but strong baseline that makes fine-tuning BERT-based models significantly more stable than previously proposed approaches. Code to reproduce our results is available online: https://github.com/uds-lsv/bert-stable-fine-tuning.Preprint. Under review. | On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines |
d17711681 | Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired maximum entropy distribution. Doing so is nontrivial in that the objective being maximized (entropy) is a function of the density itself. By exploiting recent developments in normalizing flow networks, we cast the maximum entropy problem into a finite-dimensional constrained optimization, and solve the problem by combining stochastic optimization with the augmented Lagrangian method. Simulation results demonstrate the effectiveness of our method, and applications to finance and computer vision show the flexibility and accuracy of using maximum entropy flow networks. * These authors contributed equally. | MAXIMUM ENTROPY FLOW NETWORKS |
d247450890 | Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting. In this paper, we introduce a deep expansion learning framework, DEPTS, for PTS forecasting. DEPTS starts with a decoupled formulation by introducing the periodic state as a hidden variable, which stimulates us to make two dedicated modules to tackle the aforementioned two challenges. First, we develop an expansion module on top of residual learning to perform a layer-by-layer expansion of those complicated dependencies. Second, we introduce a periodicity module with a parameterized periodic function that holds sufficient capacity to capture diversified periods. Moreover, our two customized modules also have certain interpretable capabilities, such as attributing the forecasts to either local momenta or global periodicity and characterizing certain core periodic properties, e.g., amplitudes and frequencies. Extensive experiments on both synthetic data and real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most cases, DEPTS achieves significant improvements over the best baseline. Specifically, the error reduction can even reach up to 20% for a few cases. All codes are publicly available at | DEPTS: DEEP EXPANSION LEARNING FOR PERIODIC TIME SERIES FORECASTING |
d252681067 | Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for humans in daily life, it remains a challenge for large language models (LLMs) that lack a deep understanding of the cause-effect relations in procedures. Previous methods require manual exemplars to acquire procedural knowledge from LLMs in the zero-shot setting. However, such elicited pre-trained knowledge in LLMs induces spurious correlations between goals and steps, impairing the model's generalization to unseen tasks. In contrast, this paper proposes a neuro-symbolic procedural PLANner (PLAN) that elicits procedural knowledge from the LLMs with commonsense-infused prompting. To mitigate spurious goal-step correlations, we use symbolic program executors on the latent procedural representations to formalize prompts from commonsense knowledge bases as a causal intervention toward the Structural Causal Model of procedural planning. Both automatic and human evaluations on WikiHow and RobotHow show the superiority of PLAN on procedural planning without further training or manual exemplars. | NEURO-SYMBOLIC PROCEDURAL PLANNING WITH COMMONSENSE PROMPTING |
d201646137 | In seeking for sparse and efficient neural network models, many previous works investigated on enforcing 1 or 0 regularizers to encourage weight sparsity during training. The 0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values. But it cannot provide useful gradients and therefore requires complex optimization techniques. The 1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent. Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity. Inspired by the Hoyer measure (the ratio between 1 and 2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant. Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level. We also show that DeepHoyer can be applied to both element-wise and structural pruning. The codes are available atPublished as a conference paper at ICLR 2020 ADMM) . These additional measures brought overheads to the optimization process, making the use of these methods on larger networks difficult. To achieve even sparser neural networks, we argue to move beyond 0 and 1 regularizers and seek for a sparsity-inducing regularizer that is both almost everywhere differentiable (like 1 ) and scale-invariant (like 0 ). . Learning sparse neural networks through l_0 regularization. arXiv preprint arXiv:1712.01312, 2017b.Jian-Hao Luo and Jianxin Wu. Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference. arXiv preprint arXiv:1805.08941, 2018.Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. Thinet: A filter level pruning method for deep neural network compression. In | DEEPHOYER: LEARNING SPARSER NEURAL NETWORK WITH DIFFERENTIABLE SCALE-INVARIANT SPARSITY MEASURES |
d202888483 | A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning. | THE IMPLICIT BIAS OF DEPTH: HOW INCREMENTAL LEARNING DRIVES GENERALIZATION |
d256105245 | Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class distributions of local datasets. In recent years, personalized federated learning (pFL) has emerged as a potential solution to the challenges presented by heterogeneous data. However, existing pFL methods typically enhance performance of local models at the expense of the global model's accuracy. We propose FedHKD (Federated Hyper-Knowledge Distillation), a novel FL algorithm in which clients rely on knowledge distillation (KD) to train local models. In particular, each client extracts and sends to the server the means of local data representations and the corresponding soft predictions -information that we refer to as "hyper-knowledge". The server aggregates this information and broadcasts it to the clients in support of local training. Notably, unlike other KD-based pFL methods, FedHKD does not rely on a public dataset nor it deploys a generative model at the server. We analyze convergence of FedHKD and conduct extensive experiments on visual datasets in a variety of scenarios, demonstrating that FedHKD provides significant improvement in both personalized as well as global model performance compared to state-of-the-art FL methods designed for heterogeneous data settings. | THE BEST OF BOTH WORLDS: ACCURATE GLOBAL AND PERSONALIZED MODELS THROUGH FEDERATED LEARNING WITH DATA-FREE HYPER-KNOWLEDGE DISTILLATION |
d252089864 | Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVRP and Catalyzed SVRP. This second-order similarity condition has grown popular recently, and is satisfied in many applications including distributed statistical learning and differentially private empirical risk minimization. The first algorithm, SVRP, combines approximate stochastic proximal point evaluations, client sampling, and variance reduction. We show that SVRP is communication efficient and achieves superior performance to many existing algorithms when function similarity is high enough. Our second algorithm, Catalyzed SVRP, is a Catalyst-accelerated variant of SVRP that achieves even better performance and uniformly improves upon existing algorithms for federated optimization under second-order similarity and strong convexity. In the course of analyzing these algorithms, we provide a new analysis of the Stochastic Proximal Point Method (SPPM) that might be of independent interest. Our analysis of SPPM is simple, allows for approximate proximal point evaluations, does not require any smoothness assumptions, and shows a clear benefit in communication complexity over ordinary distributed stochastic gradient descent.arXiv:2209.02257v2 [cs.LG] 23 May 2023We answer the above question in the affirmative and show the utility of using client sampling in optimization under second-order similarity for strongly convex objectives. Our main contributions are as follows:• A new algorithm for federated optimization (SVRP, Algorithm 2). We develop a new algorithm, SVRP (Stochastic Variance-Reduced Proximal Point), that utilizes client sampling to improve upon the existing algorithms for solving Problem 1 under second-order similarity. SVRP has a better dependence on the number of clients M in its communication complexity than all existing algorithms (seeTable 1), and achieves superior performance when the dissimilarity constant δ is small enough. SVRP trades off a higher computational complexity for less communication.• Catalyst-accelerated SVRP. By using Catalyst (Lin et al., 2015), we accelerate SVRP and obtain a new algorithm (Catalyzed SVRP) that improves the dependence on the effective conditioning from δ 2 µ 2 to δ µ . Catalyzed SVRP also has a better convergence rate (in number of communication steps, ignoring constants and logarithmic factors) than all existing accelerated algorithms for this problem under Assumption 1, reducing the dependence on the number of clients multiplied by the effective conditioning δ µ from δ µ M to δ µ M 3/4 (seeTable 1).While both SVRP and Catalyzed SVRP achieve a communication complexity that is better than algorithms designed for the standard finite-sum setting (like SVRG or SAGA), the computational complexity is a lot worse. This is because we tradeoff local computation complexity for a reduced communication complexity. Additionally, both SVRP and Catalyzed SVRP are based upon a novel combination of variance-reduction techniques and the stochastic proximal point method (SPPM). SPPM is our starting point, and we provide a new analysis for it that might be of independent interest. Our analysis of SPPM is simple, allows for approximate evaluations of the proximal operator, and extends to include variance reduction. In Section 15 we also consider the more general constrained optimization problem and provide similar convergence rates in that setting.Related workDistributed optimization under Assumption 1. There is a long line of work analyzing distributed optimization under Assumption 1 and strong convexity: Shamir et al. (2014) first gave DANE and analyzed it for quadratics, and showed the benefits of using second-order similarity in the setting of statistical learning for quadratic objectives. Zhang and Lin(2015)developed the DiSCO algorithm that improved upon DANE for quadratics, and also analyzed it for self-concordant objectives. Arjevani and Shamir(2015)gave a lower bound that matched the rate given by DANE, though without allowing for client sampling. The theory of DANE was later improved in (Yuan and Li, 2019), allowing for local convergence for non-quadratic objectives. Another algorithm SCAFFOLD (Karimireddy et al., 2020b) can be seen as a variant of DANE and is also analyzed for quadratics. In the context of decentralized optimization, Sun et al. (2022) gave SONATA and showed a similar rate to DANE but for general strongly convex objectives, then Tian et al. (2022) improved the convergence rate of SONATA by acceleration. Finally, Kovalev et al. (2022) improved the convergence rate of accelerated SONATA even further by removing extra logarithmic factors.We give an overview of all related results inTable 1and provide more thorough comparisons in the theory and algorithms section. | Faster federated optimization under second-order similarity |
d256389917 | Real-world deployment of machine learning models is challenging because data evolves over time.While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to address it.This paper addresses situations when data evolves gradually.We introduce a time-varying propensity score that can detect gradual shifts in the distribution of data which allows us to selectively sample past data to update the model-not just similar data from the past like that of a standard propensity score but also data that evolved in a similar fashion in the past.The time-varying propensity score is quite general: we demonstrate different ways of implementing it and evaluate it on a variety of problems ranging from supervised learning (e.g., image classification problems) where data undergoes a sequence of gradual shifts, to reinforcement learning tasks (e.g., robotic manipulation and continuous control) where data shifts as the policy or the task changes. | TIME-VARYING PROPENSITY SCORE TO BRIDGE THE GAP BETWEEN THE PAST AND PRESENT |
d199528271 | The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -its variance is problematically large in the early stage, and presume warmup works as a variance reduction technique. We provide both empirical and theoretical evidence to verify our hypothesis. We further propose Rectified Adam (RAdam), a novel variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the efficacy and robustness of RAdam. 1 * Work was done during an internship at Microsoft Dynamics 365 AI. † Work was done during an internship at Microsoft Dynamics 365 AI. 1 All implementations are available at: https://github.com/LiyuanLucasLiu/RAdam. arXiv:1908.03265v4 [cs.LG] 26 Oct 2021Published as a conference paper at ICLR 2020 conduct warmup. Thus, researchers typically use different settings in different applications and have to take a trial-and-error approach, which can be tedious and time-consuming. | ON THE VARIANCE OF THE ADAPTIVE LEARNING RATE AND BEYOND |
d238353966 | Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient than non-ensemble counterparts such as Soft Actor-Critic (SAC) (Haarnoja et al., 2018a). To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions. Our dropout Q-functions are simple Q-functions equipped with dropout connection and layer normalization. Despite its simplicity of implementation, our experimental results indicate that DroQ is doubly (sample and computationally) efficient. It achieved comparable sample efficiency with REDQ, much better computational efficiency than REDQ, and comparable computational efficiency with that of SAC. | DROPOUT Q-FUNCTIONS FOR DOUBLY EFFICIENT REINFORCEMENT LEARNING |
d260900008 | Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the Code Usage Frequency of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit code-based self-verification (CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as "False", the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset (53.9% → 84.3%). | SOLVING CHALLENGING MATH WORD PROBLEMS USING GPT-4 CODE INTERPRETER WITH CODE-BASED SELF-VERIFICATION |
d202539918 | Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur. Recurrent networks, which have enjoyed a modicum of success, still lack the generalization and systematicity ultimately required for modelling language. In this work, we propose an extension to the venerable Long Short-Term Memory in the form of mutual gating of the current input and the previous output. This mechanism affords the modelling of a richer space of interactions between inputs and their context. Equivalently, our model can be viewed as making the transition function given by the LSTM context-dependent. Experiments demonstrate markedly improved generalization on language modelling in the range of 3-4 perplexity points on Penn Treebank and Wikitext-2, and 0.01-0.05 bpc on four character-based datasets. We establish a new state of the art on all datasets with the exception of Enwik8, where we close a large gap between the LSTM and Transformer models. | Mogrifier LSTM |
d221640669 | Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a recent approach to generative modeling. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Fréchet Inception Distance, a popular metric for generative models. We show that this apparent gap vanishes when denoising the final Langevin samples using the score network. In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed Langevin Sampling, and 2) a hybrid training formulation, composed of both denoising score matching and adversarial objectives. By combining both of these techniques and exploring different network architectures, we elevate score matching methods and obtain results competitive with state-of-the-art image generation on CIFAR-10. | ADVERSARIAL SCORE MATCHING AND IMPROVED SAMPLING FOR IMAGE GENERATION |
d263608898 | The effect of underrepresentation on the performance of minority groups is known to be a serious problem in supervised learning settings; however, it has been underexplored so far in the context of self-supervised learning (SSL).In this paper, we demonstrate that contrastive learning (CL), a popular variant of SSL, tends to collapse representations of minority groups with certain majority groups.We refer to this phenomenon as representation harm and demonstrate it on image and text datasets using the corresponding popular CL methods.Furthermore, our causal mediation analysis of allocation harm on a downstream classification task reveals that representation harm is partly responsible for it, thus emphasizing the importance of studying and mitigating representation harm.Finally, we provide a theoretical explanation for representation harm using a stochastic block model that leads to a representational neural collapse in a contrastive learning setting. | An Investigation of Representation and Allocation Harms in Contrastive Learning |
d259076379 | Recent advances in large language model (LLM) pretraining have led to highquality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. However, quantization down to 3-4 bits per parameter usually leads to moderate-to-high accuracy losses, especially for smaller models in the 1-10B parameter range, which are well-suited for edge deployments. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique which enables for the first time near-lossless compression of LLMs across model scales, while reaching similar compression levels to previous methods. SpQR works by identifying and isolating outlier weights, which cause particularlylarge quantization errors, and storing them in higher precision, while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than 1% in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run 33B parameter LLM on a single 24 GB consumer GPU without any performance degradation at 15% speedup thus making powerful LLMs available to consumer without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime 3 . Specifically, we provide an efficient GPU inference algorithm for SpQR which yields faster inference than 16-bit baselines at similar accuracy, while enabling memory compression gains of more than 4x. | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression |
d212657478 | We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime. We formulate the problem of generating curious behavior as one of meta-learning: an outer loop will search over a space of curiosity mechanisms that dynamically adapt the agent's reward signal, and an inner loop will perform standard reinforcement learning using the adapted reward signal. However, current meta-RL methods based on transferring neural network weights have only generalized between very similar tasks. To broaden the generalization, we instead propose to meta-learn algorithms: pieces of code similar to those designed by humans in ML papers. Our rich language of programs combines neural networks with other building blocks such as buffers, nearest-neighbor modules and custom loss functions. We demonstrate the effectiveness of the approach empirically, finding two novel curiosity algorithms that perform on par or better than human-designed published curiosity algorithms in domains as disparate as grid navigation with image inputs, acrobot, lunar lander, ant and hopper. * Equal contribution. | META-LEARNING CURIOSITY ALGORITHMS |
d227161986 | We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, it utilizes predictability to discover object keypoints, an intrinsic property of objects. This ensures that it does not overly bias keypoints to focus on characteristics that are not unique to objects, such as movement, shape, colour etc. We demonstrate the efficacy of PermaKey on Atari where it learns keypoints corresponding to the most salient object parts and is robust to certain visual distractors. Further, on downstream RL tasks in the Atari domain we demonstrate how agents equipped with our keypoints outperform those using competing alternatives, even on challenging environments with moving backgrounds or distractor objects. | UNSUPERVISED OBJECT KEYPOINT LEARNING USING LOCAL SPATIAL PREDICTABILITY |
d259095948 | Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like characteristics. In the present paper, we address this gap and ask whether large language models can be turned into cognitive models. We find that -after finetuning them on data from psychological experiments -these models offer accurate representations of human behavior, even outperforming traditional cognitive models in two decision-making domains. In addition, we show that their representations contain the information necessary to model behavior on the level of individual subjects. Finally, we demonstrate that finetuning on multiple tasks enables large language models to predict human behavior in a previously unseen task. Taken together, these results suggest that large, pre-trained models can be adapted to become generalist cognitive models, thereby opening up new research directions that could transform cognitive psychology and the behavioral sciences as a whole.Preprint. Under review. | Turning large language models into cognitive models |
d208006294 | The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase interactions.Existing flat, word level explanations of predictions hardly unveil how neural networks handle compositional semantics to reach predictions.To tackle the challenge, we study hierarchical explanation of neural network predictions.We identify non-additivity and independent importance attributions within hierarchies as two desirable properties for highlighting word and phrase interactions.We show prior efforts on hierarchical explanations, e.g.contextual decomposition, however, do not satisfy the desired properties mathematically.In this paper, we propose a formal way to quantify the importance of each word or phrase for hierarchical explanations.Following the formulation, we propose Sampling and Contextual Decomposition (SCD) algorithm and Sampling and Occlusion (SOC) algorithm.Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms.Our algorithms apply to hierarchical visualization of compositional semantics, extraction of classification rules and improving human trust of models. | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models |
d251252927 | Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classification problem of a non-linearly separable Gaussian mixture model coupled with a stochastic block model. First, we show that a single graph convolution expands the regime of the distance between the means where multi-layer networks can classify the data by a factor of at least 1/ 4 √ Edeg, where Edeg denotes the expected degree of a node. Second, we show that with a slightly stronger graph density, two graph convolutions improve this factor to at least 1/ 4 √ n, where n is the number of nodes in the graph. Finally, we provide both theoretical and empirical insights into the performance of graph convolutions placed in different combinations among the layers of a network, concluding that the performance is mutually similar for all combinations of the placement. We present extensive experiments on both synthetic and real-world data that illustrate our results. | Effects of Graph Convolutions in Multi-layer Networks |
d3475375 | We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training. 1 We base this name on the Chekhov's gun (dramatic) principle that states that every element in a story must be necessary, and irrelevant elements should be removed. Analogously, our Chekhov GAN algorithm introduces a sequence of elements which are eventually composed to yield a generator. | An Online Learning Approach to Generative Adversarial Networks |
d108306455 | We introduce an approach to model surface properties governing bounces in everyday scenes. Our model learns end-to-end, starting from sensor inputs, to predict post-bounce trajectories and infer two underlying physical properties that govern bouncing -restitution and effective collision normals. Our model, Bounce and Learn, comprises two modules -a Physics Inference Module (PIM) and a Visual Inference Module (VIM). VIM learns to infer physical parameters for locations in a scene given a single still image, while PIM learns to model physical interactions for the prediction task given physical parameters and observed pre-collision 3D trajectories. To achieve our results, we introduce the Bounce Dataset comprising 5K RGB-D videos of bouncing trajectories of a foam ball to probe surfaces of varying shapes and materials in everyday scenes including homes and offices. Our proposed model learns from our collected dataset of real-world bounces and is bootstrapped with additional information from simple physics simulations. We show on our newly collected dataset that our model out-performs baselines, including trajectory fitting with Newtonian physics, in predicting post-bounce trajectories and inferring physical properties of a scene. * Work done at Adobe Research during summer internship. Dataset and code available here: . Dynamics-aware numerical coarsening for fabrication design. | BOUNCE AND LEARN: MODELING SCENE DYNAMICS WITH REAL-WORLD BOUNCES |
d256194054 | Figure 1: Extrapolating from one image. Strongly augmented patches from a single image are used to train a student (S) to distinguish semantic classes, such as those in ImageNet. The student neural network is initialized randomly and learns from a pretrained teacher (T) via KL-divergence. Although almost none of target categories are present in the image, we find student performances of > 69% for classifying ImageNet's 1000 classes. In this paper, we develop this single datum learning framework and investigate it across datasets and domains.ABSTRACT What can neural networks learn about the visual world when provided with only a single image as input? While any image obviously cannot contain the multitudes of all existing objects, scenes and lighting conditions -within the space of all 256 3·224·224 possible 224-sized square images, it might still provide a strong prior for natural images. To analyze this "augmented image prior" hypothesis, we develop a simple framework for training neural networks from scratch using a single image and augmentations using knowledge distillation from a supervised pretrained teacher. With this, we find the answer to the above question to be: 'surprisingly, a lot'. In quantitative terms, we find accuracies of 94%/74% on CIFAR-10/100, 69% on ImageNet, and by extending this method to video and audio, 51% on Kinetics-400 and 84% on SpeechCommands. In extensive analyses spanning 13 datasets, we disentangle the effect of augmentations, choice of data and network architectures and also provide qualitative evaluations that include lucid "panda neurons" in networks that have never even seen one. arXiv:2112.00725v4 [cs.CV] 24 Jan 2023 1. A minimal framework for training neural networks with a single datum using distillation. 2. Extensive ablations of the proposed method, such as the dependency on the source image, augmentations and network architectures. 3. Large scale empirical evidence of neural networks' ability to extrapolate on > 12 vision and audio datasets. 4. Qualitative insights on what and how neural networks trained with a single image learn. | THE AUGMENTED IMAGE PRIOR: DISTILLING 1000 CLASSES BY EXTRAPOLATING FROM A SINGLE IMAGE |
d249431433 | The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade. Specifically, in terms of robustness on out-of-distribution samples, recent research finds that Transformers are inherently more robust than CNNs, regardless of different training setups. Moreover, it is believed that such superiority of Transformers should largely be credited to their self-attention-like architectures per se. In this paper, we question that belief by closely examining the design of Transformers. Our findings lead to three highly effective architecture designs for boosting robustness, yet simple enough to be implemented in several lines of code, namely a) patchifying input images, b) enlarging kernel size, and c) reducing activation layers and normalization layers. Bringing these components together, we are able to build pure CNN architectures without any attention-like operations that are as robust as, or even more robust than, Transformers. We hope this work can help the community better understand the design of robust neural architectures. The code is publicly available at https://github.com/UCSC-VLAA/RobustCNN. , et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021. . Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In ICLR, 2018.Igor Gitman and Boris Ginsburg. Comparison of batch normalization and weight normalization algorithms for the large-scale image classification. arXiv preprint arXiv:1709.08145, 2017. | CAN CNNS BE MORE ROBUST THAN TRANSFORMERS? |
d261823404 | Creating diverse and high-quality 3D assets with an automatic generative model is highly desirable.Despite extensive efforts on 3D generation, most existing works focus on the generation of a single category or a few categories.In this paper, we introduce a diffusion-based feed-forward framework for synthesizing massive categories of real-world 3D objects with a single generative model.Notably, there are three major challenges for this large-vocabulary 3D generation: a) the need for expressive yet efficient 3D representation; b) large diversity in geometry and texture across categories; c) complexity in the appearances of real-world objects.To this end, we propose a novel triplane-based 3D-aware Diffusion model with TransFormer, DiffTF, for handling challenges via three aspects.1) Considering efficiency and robustness, we adopt a revised triplane representation and improve the fitting speed and accuracy.2) To handle the drastic variations in geometry and texture, we regard the features of all 3D objects as a combination of generalized 3D knowledge and specialized 3D features.To extract generalized 3D knowledge from diverse categories, we propose a novel 3D-aware transformer with shared cross-plane attention.It learns the cross-plane relations across different planes and aggregates the generalized 3D knowledge with specialized 3D features.3) In addition, we devise the 3D-aware encoder/decoder to enhance the generalized 3D knowledge in the encoded triplanes for handling categories with complex appearances.Extensive experiments on ShapeNet and OmniObject3D (over 200 diverse real-world categories) convincingly demonstrate that a single DiffTF model achieves state-of-the-art large-vocabulary 3D object generation performance with large diversity, rich semantics, and high quality.Our project page: https:// ziangcao0312.github.io/difftf_pages/. | LARGE-VOCABULARY 3D DIFFUSION MODEL WITH TRANSFORMER |
d257495957 | The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning. OTOv2 is automatic and pluggable into various deep learning applications, and requires almost minimal engineering efforts from the users. Methodologically, OTOv2 proposes two major improvements: (i) Autonomy: automatically exploits the dependency of general DNNs, partitions the trainable variables into Zero-Invariant Groups (ZIGs), and constructs the compressed model; and (ii) Dual Half-Space Projected Gradient (DHSPG): a novel optimizer to more reliably solve structured-sparsity problems. Numerically, we demonstrate the generality and autonomy of OTOv2 on a variety of model architectures such as VGG, ResNet, CARN, ConvNeXt, DenseNet and StackedUnets, the majority of which cannot be handled by other methods without extensive handcrafting efforts. Together with benchmark datasets including CIFAR10/100, DIV2K, Fashion-MNIST, SVNH and ImageNet, its effectiveness is validated by performing competitively or even better than the state-of-the-arts. The source code is available at https | OTOV2: AUTOMATIC, GENERIC, USER-FRIENDLY |
d68111200 | Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. We first conduct an empirical study to show that vanilla quantization suffers more from adversarial attacks. We observe that the inferior robustness comes from the error amplification effect, where the quantization operation further enlarges the distance caused by amplified noise. Then we propose a novel Defensive Quantization (DQ) method by controlling the Lipschitz constant of the network during quantization, such that the magnitude of the adversarial noise remains non-expansive during inference. Extensive experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization method can defend neural networks against adversarial examples, and even achieves superior robustness than their fullprecision counterparts, while maintaining the same hardware efficiency as vanilla quantization approaches. As a by-product, DQ can also improve the accuracy of quantized models without adversarial attack. | DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS |
d258887825 | Minimax problems are notoriously challenging to optimize. However, we demonstrate that the two-timescale extragradient can be a viable solution. By utilizing dynamical systems theory, we show that it converges to points that satisfy the second-order necessary condition of local minimax points, under a mild condition. This work surpasses all previous results as we eliminate a crucial assumption that the Hessian, with respect to the maximization variable, is nondegenerate.Preprint. Under review. | Two-timescale Extragradient for Finding Local Minimax Points |
d263829263 | We contribute to the study of formal languages that can be recognized by transformer encoders.We focus on two self-attention mechanisms: (1) UHAT (Unique Hard Attention Transformers) and (2) AHAT (Average Hard Attention Transformers).UHAT encoders are known to recognize only languages inside the circuit complexity class AC 0 , i.e., accepted by a family of poly-sized and depth-bounded boolean circuits with unbounded fan-ins.On the other hand, AHAT encoders can recognize languages outside AC 0 ), but their expressive power still lies within the bigger circuit complexity class TC 0 , i.e., AC 0 -circuits extended by majority gates.We first show a negative result that there is an AC 0 -language that cannot be recognized by an UHAT encoder.On the positive side, we show that UHAT encoders can recognize a rich fragment of AC 0 -languages, namely, all languages definable in first-order logic with arbitrary unary numerical predicates.This logic, includes, for example, all regular languages from AC 0 .We then show that AHAT encoders can recognize all languages of our logic even when we enrich it with counting terms.We apply these results to derive new results on the expressive power of UHAT and AHAT up to permutation of letters (a.k.a.Parikh images). | Logical Languages Accepted by Transformer Encoders with Hard Attention |
d3568073 | We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CELEBA images at 1024 2 . We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CELEBA dataset. | PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION |
d204401860 | We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies. Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation. We propose estimated mixture policy (EMP), a novel class of partially policy-agnostic methods to accurately estimate those quantities. With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction. In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods. * Hongyuan zha is on leave from Georgia Institute of technology. Part of this work was done while lu wang, yizhe hang were visiting the Chinese university of hong kong, Shenzhen arXiv:1910.04849v1 [cs.LG] 10 Oct 2019Dietterich and Thomas G. Hierarchical reinforcement learning with the maxq value function decomposition. | INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE BEHAVIOR POLICIES |
d212414722 | Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning.Most of the previous models have been shown to work only on scenes with a few objects.In this paper, we propose SCALOR, a probabilistic generative world model for learning SCALable Object-oriented Representation of a video.With the proposed spatiallyparallel attention and proposal-rejection mechanisms, SCALOR can deal with orders of magnitude larger numbers of objects compared to the previous stateof-the-art models.Additionally, we introduce a background module that allows SCALOR to model complex dynamic backgrounds as well as many foreground objects in the scene.We demonstrate that SCALOR can deal with crowded scenes containing up to a hundred objects while jointly modeling complex dynamic backgrounds.Importantly, SCALOR is the first unsupervised object representation model shown to work for natural scenes containing several tens of moving objects. | SCALOR: GENERATIVE WORLD MODELS WITH SCALABLE OBJECT REPRESENTATIONS |
d262054258 | It has long been established that predictive models can be transformed into lossless compressors and vice versa.Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models.Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors.In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models.We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model. | Language Modeling Is Compression |
d209439843 | State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings. Radev. Improving text-to-SQL evaluation methodology. In ACL, 2018. URL http://aclweb.org/anthology/P18-1033.Jerry A Fodor and Zenon W Pylyshyn. Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2):3-71, 1988. URL https://pdfs.semanticscholar.org/d806/ 76034bfabfea59f35698af0f715a555fcf50.pdf. | MEASURING COMPOSITIONAL GENERALIZATION: A COMPREHENSIVE METHOD ON REALISTIC DATA |
d53831933 | We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video summarizing our results. * Equal contribution. | NEURAL PROBABILISTIC MOTOR PRIMITIVES FOR HUMANOID CONTROL |
d246442021 | Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low-dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data.We present a method for learning counterfactual reasoning of physical processes in pixel space, which requires the prediction of the impact of interventions on initial conditions.Going beyond the identification of structural relationships, we deal with the challenging problem of forecasting raw video over long horizons.Our method does not require the knowledge or supervision of any ground truth positions or other object or scene properties.Our model learns and acts on a suitable hybrid latent representation based on a combination of dense features, sets of 2D keypoints and an additional latent vector per keypoint.We show that this better captures the dynamics of physical processes than purely dense or sparse representations.We introduce a new challenging and carefully designed counterfactual benchmark for predictions in pixel space and outperform strong baselines in physics-inspired ML and video prediction. | FILTERED-COPHY: UNSUPERVISED LEARNING OF COUNTERFACTUAL PHYSICS IN PIXEL SPACE |
d4862861 | Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning procedure called Mechanical Turker Descent (MTD) and use it to train agents to execute natural language commands grounded in a fantasy text adventure game. In MTD, Turkers compete to train better agents in the short term, and collaborate by sharing their agents' skills in the long term. This results in a gamified, engaging experience for the Turkers and a better quality teaching signal for the agents compared to static datasets, as the Turkers naturally adapt the training data to the agent's abilities. | MASTERING THE DUNGEON: GROUNDED LANGUAGE LEARNING BY MECHANICAL TURKER DESCENT |
d247594872 | Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph. However, distributed GCN training incurs prohibitive overhead of communicating node features and feature gradients among partitions for every GCN layer during each training iteration, limiting the achievable training efficiency and model scalability. To this end, we propose PipeGCN, a simple yet effective scheme that hides the communication overhead by pipelining inter-partition communication with intra-partition computation. It is non-trivial to pipeline for efficient GCN training, as communicated node features/gradients will become stale and thus can harm the convergence, negating the pipeline benefit. Notably, little is known regarding the convergence rate of GCN training with both stale features and stale feature gradients. This work not only provides a theoretical convergence analysis but also finds the convergence rate of PipeGCN to be close to that of the vanilla distributed GCN training without any staleness. Furthermore, we develop a smoothing method to further improve PipeGCN's convergence. Extensive experiments show that PipeGCN can largely boost the training throughput (1.7×∼28.5×) while achieving the same accuracy as its vanilla counterpart and existing full-graph training methods. The code is available at https://github.com/RICE-EIC/PipeGCN. This approach first partitions a giant graph into multiple small subgraps, each of which is able to fit into a single GPU, and then train these partitioned subgraphs locally on GPUs together with indispensable communication across partitions. Following this direction, several recent works (Ma et al., 2019; Jia et al., 2020; Tripathy et al., 2020; Thorpe et al., 2021; Wan et al., 2022) have been proposed and verified the great potential of distributed GCN training. P 3 (Gandhi & Iyer, 2021) follows another direction that splits the data along the feature dimension and leverages intra-layer model parallelism for training, which shows superior performance on small models.In this work, we propose a new method for distributed GCN training, PipeGCN, which targets achieving a full-graph accuracy with boosted training efficiency. Our main contributions are following:• We first analyze two efficiency bottlenecks in distributed GCN training: the required significant communication overhead and frequent synchronization, and then propose a simple yet effective technique called PipeGCN to address both aforementioned bottlenecks by pipelining inter-partition communication with intra-partition computation to hide the communication overhead. • We address the challenge raised by PipeGCN, i.e., the resulting staleness in communicated features and feature gradients (neither weights nor weight gradients), by providing a theoretical convergence analysis and showing that PipeGCN's convergence rate is O(T − 2 3 ), i.e., close to vanilla distributed GCN training without staleness. To the best of our knowledge, we are the first to provide a theoretical convergence proof of GCN training with both stale feature and stale feature gradients.• We further propose a low-overhead smoothing method to further improve PipeGCN's convergence by reducing the error incurred by the staleness. • Extensive empirical and ablation studies consistently validate the advantages of PipeGCN over both vanilla distributed GCN training and those SOTA full-graph training methods (e.g., boosting the training throughput by 1.7×∼28.5× while achieving the same or a better accuracy).BACKGROUND AND RELATED WORKSGraph Convolutional Networks. GCNs represent each node in a graph as a feature (embedding) vector and learn the feature vector via a two-step process (neighbor aggregation and then node update) for each layer, which can be mathematically described as:network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks. In Proceedings of the 51st IEEE/ACM International Symposium on Microarchitecture (MICRO'18), Fukuoka City, Japan, 2018a. Pipe-SGD: A decentralized pipelined sgd framework for distributed deep net training. Advances in Neural Information Processing Systems, 2018b. Renjie Liao, Raquel Urtasun, and Richard Zemel. A pac-bayesian approach to generalization bounds for graph neural networks. arXiv preprint arXiv:2012.07690, 2020. , et al. Dorylus: affordable, scalable, and accurate gnn training with distributed cpu servers and serverless threads. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), pp. 495-514, 2021. Alok Tripathy, Katherine Yelick, and Aydin Buluc. Reducing communication in graph neural network training. arXiv preprint arXiv:2005.03300, 2020. | PIPEGCN: EFFICIENT FULL-GRAPH TRAINING OF GRAPH CONVOLUTIONAL NETWORKS WITH PIPELINED FEATURE COMMUNICATION |
d252873057 | While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical graph with maximal score for a given class, completely missing any combinatorial aspect that the GNN could have learned. In this work, we propose GLGExplainer (Global Logic-based GNN Explainer), the first Global Explainer capable of generating explanations as arbitrary Boolean combinations of learned graphical concepts. GLGExplainer is a fully differentiable architecture that takes local explanations as inputs and combines them into a logic formula over graphical concepts, represented as clusters of local explanations. Contrary to existing solutions, GLGExplainer provides accurate and human-interpretable global explanations that are perfectly aligned with ground-truth explanations (on synthetic data) or match existing domain knowledge (on real-world data). Extracted formulas are faithful to the model predictions, to the point of providing insights into some occasionally incorrect rules learned by the model, making GLGExplainer a promising diagnostic tool for learned GNNs. | GLOBAL EXPLAINABILITY OF GNNS VIA LOGIC COMBINATION OF LEARNED CONCEPTS |
d260203353 | Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation.This duality requires an inherently invertible architecture.However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve good results.Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention.In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework.Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency.Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability. | Kernelised Normalising Flows |
d238744039 | With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently involves training on noisy inputs. Unfortunately, source code is highly sensitive to small changes; a single token can result in compilation failures or erroneous programs, unlike natural languages where small inaccuracies may not change the meaning of a sentence. To address this issue, we propose to leverage an automated unit-testing system to filter out invalid translations, thereby creating a fully tested parallel corpus. We found that fine-tuning an unsupervised model with this filtered data set significantly reduces the noise in the translations so-generated, comfortably outperforming the state-of-the-art for all language pairs studied. In particular, for Java → Python and Python → C++ we outperform the best previous methods by more than 16% and 24% respectively, reducing the error rate by more than 35%. † Work done while at Facebook | LEVERAGING AUTOMATED UNIT TESTS FOR UNSUPERVISED CODE TRANSLATION |
d208910151 | We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call "optimistic closure," which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic closure, we prove that our algorithm enjoys a regret bound ofÕ(where d is the dimensionality of the state-action features and T is the number of episodes. This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions. | Optimism in Reinforcement Learning with Generalized Linear Function Approximation |
d213392702 | Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are vulnerable to adversarial attacks in which small input perturbations can produce catastrophic misclassifications. In this work, we propose EMPIR, ensembles of quantized DNN models with different numerical precisions, as a new approach to increase robustness against adversarial attacks. EMPIR is based on the observation that quantized neural networks often demonstrate much higher robustness to adversarial attacks than full precision networks, but at the cost of a substantial loss in accuracy on the original (unperturbed) inputs. EM-PIR overcomes this limitation to achieve the "best of both worlds", i.e., the higher unperturbed accuracies of the full precision models combined with the higher robustness of the low precision models, by composing them in an ensemble. Further, as low precision DNN models have significantly lower computational and storage requirements than full precision models, EMPIR models only incur modest compute and memory overheads compared to a single full-precision model (<25% in our evaluations). We evaluate EMPIR across a suite of DNNs for 3 different image recognition tasks (MNIST, CIFAR-10 and ImageNet) and under 4 different adversarial attacks. Our results indicate that EMPIR boosts the average adversarial accuracies by 42.6%, 15.2% and 10.5% for the DNN models trained on the MNIST, CIFAR-10 and ImageNet datasets respectively, when compared to single full-precision models, without sacrificing accuracy on the unperturbed inputs. | EMPIR: ENSEMBLES OF MIXED PRECISION DEEP NETWORKS FOR INCREASED ROBUSTNESS AGAINST ADVERSARIAL ATTACKS |
d219401642 | Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable monotonic interpolation scheme to predict the duration of each input token. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion score exceeding 4 on a 5 point scale, which is comparable to the state-of-the-art models relying on multi-stage training and additional supervision. 1 * Equal contribution. First author determined by coin toss. 1 Listen to our model reading this abstract at: https://deepmind.com/research/publications/End-to-End-Adversarial-Text-to-Speech | End-to-End Adversarial Text-to-Speech |
d259137503 | We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient crosswindow communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy vs. image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at https://github.com/NVlabs/FasterViT. | FasterViT: Fast Vision Transformers with Hierarchical Attention |
d256105681 | The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly.Silvia Chiappa. Path-specific counterfactual fairness. In | TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAUSAL FACTORS |
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