We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understood, there is no consensus on how to quantify the distance from perfect calibration. Numerous calibration measures have been proposed in the literature, but it is unclear how they compare to each other, and many popular measures such as Expected Calibration Error (ECE) fail to satisfy basic properties like continuity. We present a rigorous framework for analyzing calibration measures, inspired by the literature on property testing. We propose a ground-truth notion of distance from calibration: the $\ell_1$ distance to the nearest perfectly calibrated predictor. We define a consistent calibration measure as one that is a polynomial factor approximation to the this distance. Applying our framework, we identify three calibration measures that are consistent and can be estimated efficiently: smooth calibration, interval calibration, and Laplace kernel calibration. The former two give quadratic approximations to the ground truth distance, which we show is information-theoretically optimal. Our work thus establishes fundamental lower and upper bounds on measuring distance to calibration, and also provides theoretical justification for preferring certain metrics (like Laplace kernel calibration) in practice.

We present a new perspective on loss minimization and the recent notion of Omniprediction through the lens of Outcome Indistingusihability. For a collection of losses and hypothesis class, omniprediction requires that a predictor provide a loss-minimization guarantee simultaneously for every loss in the collection compared to the best (loss-specific) hypothesis in the class. We present a generic template to learn predictors satisfying a guarantee we call Loss Outcome Indistinguishability. For a set of statistical tests--based on a collection of losses and hypothesis class--a predictor is Loss OI if it is indistinguishable (according to the tests) from Nature's true probabilities over outcomes. By design, Loss OI implies omniprediction in a direct and intuitive manner. We simplify Loss OI further, decomposing it into a calibration condition plus multiaccuracy for a class of functions derived from the loss and hypothesis classes. By careful analysis of this class, we give efficient constructions of omnipredictors for interesting classes of loss functions, including non-convex losses. This decomposition highlights the utility of a new multi-group fairness notion that we call calibrated multiaccuracy, which lies in between multiaccuracy and multicalibration. We show that calibrated multiaccuracy implies Loss OI for the important set of convex losses arising from Generalized Linear Models, without requiring full multicalibration. For such losses, we show an equivalence between our computational notion of Loss OI and a geometric notion of indistinguishability, formulated as Pythagorean theorems in the associated Bregman divergence. We give an efficient algorithm for calibrated multiaccuracy with computational complexity comparable to that of multiaccuracy. In all, calibrated multiaccuracy offers an interesting tradeoff point between efficiency and generality in the omniprediction landscape.

Omnipredictors（Gopalan，Kalai，Reingold，Sharan和Wieder ITCS 2021）的概念提出了一种新的损失最小化范式。与损失损失$ c $相比，无需基于已知的损失功能学习预测指标，而是可以轻松地进行后处理以最大程度地减少任何丰富的损失功能家族。已经表明，这种杂手已经存在，并暗示（对于所有凸和Lipschitz损失函数），通过算法公平文献的多核概念的概念。然而，通常情况下，所选的动作必须遵守一些其他约束（例如能力或奇偶校验约束）。总体而言，全能器的原始概念并不适用于这种良好动机和大量研究的损失最小化的背景。在本文中，我们介绍了综合器，以进行约束优化并研究其复杂性和含义。我们介绍的概念使学习者不知道后来将分配的损失函数以及后来将施加的约束，只要已知用于定义这些约束的亚群的范围。该论文显示了如何依靠适当的多核变体获得限制优化问题的全能器。对于一些有趣的约束和一般损失函数以及一般约束和一些有趣的损失函数，我们显示了如何通过多核的变体隐含的，该变体的复杂性与标准的多核电相似。我们证明，在一般情况下，标准的数学启动不足，表明全能器是通过相对于包含$ c $中所有级别假设集的类的多核算来暗示的。我们还研究了约束是群体公平概念时的含义。

许多聚类算法由某些成本函数引导，例如广泛使用的$ k $ -means成本。这些算法将数据点划分为具有经常复杂的边界的集群，在解释聚类决策时创造了困难。在最近的工作中，Dasgupta，Frost，Moshkovitz和Rashtchian（ICML 2020）引入了可解释的聚类，其中群集边界是轴并行超平面，并且通过将决策树应用于数据来获得群集。这里的核心问题是：解释性限制增加了多少成本函数的值？鉴于$ d $ -dimensional数据点，我们显示了一个有效的算法，该算法找到了可解释的群集，其$ k $ -means成本为$ k ^ {1 - 2 / d} \，\ mathrm {poly}（d \ log k）在没有可解释性约束的情况下，群集可实现的最低成本的$倍，假设$ k，d \ ge 2 $。通过Makarychev-Shan（ICML 2021），Gamlath-jia-polak-svensson（2021），或esfandiari-mirrokni - Narayanan（2021），我们得到了$ k ^ {1 - 2 / d} \，\ mathrm {polylog}（k）$的改进界限，我们为每种选择$ k，d \ ge 2 $最多可为$ k $的多对数因子。对于$ d = 2 $特别地，我们显示$ o（\ log k \ log \ log k）$绑定，在leaker和murtinho的$ o（k \ log k）$的以前最佳界限的近乎指数上（ICML 2021）。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

In this paper, we propose a novel framework dubbed peer learning to deal with the problem of biased scene graph generation (SGG). This framework uses predicate sampling and consensus voting (PSCV) to encourage different peers to learn from each other, improving model diversity and mitigating bias in SGG. To address the heavily long-tailed distribution of predicate classes, we propose to use predicate sampling to divide and conquer this issue. As a result, the model is less biased and makes more balanced predicate predictions. Specifically, one peer may not be sufficiently diverse to discriminate between different levels of predicate distributions. Therefore, we sample the data distribution based on frequency of predicates into sub-distributions, selecting head, body, and tail classes to combine and feed to different peers as complementary predicate knowledge during the training process. The complementary predicate knowledge of these peers is then ensembled utilizing a consensus voting strategy, which simulates a civilized voting process in our society that emphasizes the majority opinion and diminishes the minority opinion. This approach ensures that the learned representations of each peer are optimally adapted to the various data distributions. Extensive experiments on the Visual Genome dataset demonstrate that PSCV outperforms previous methods. We have established a new state-of-the-art (SOTA) on the SGCls task by achieving a mean of \textbf{31.6}.