pwc-archive/papers-with-abstracts huggingface dataset:
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Authors: Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux
Date: 2023-05-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Reinforcement Learning (RL)

Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient method as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Authors: Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis
Date: 2023-10-20 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Inference, Uncertainty Quantification

Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI). However, the existing algorithms can yield overconfident posteriors (Hermans *et al.*, 2022) defeating the whole purpose of credibility if the uncertainty quantification is inaccurate. We propose to include a calibration term directly into the training objective of the neural model in selected amortized SBI techniques. By introducing a relaxation of the classical formulation of calibration error we enable end-to-end backpropagation. The proposed method is not tied to any particular neural model and brings moderate computational overhead compared to the profits it introduces. It is directly applicable to existing computational pipelines allowing reliable black-box posterior inference. We empirically show on six benchmark problems that the proposed method achieves competitive or better results in terms of coverage and expected posterior density than the previously existing approaches.
Flow Matching for Scalable Simulation-Based Inference
Authors: Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
Date: 2023-05-26 00:00:00
Proceeding: NeurIPS 2023 11

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normalizing flows. Like diffusion models, and in contrast to discrete flows, flow matching allows for unconstrained architectures, providing enhanced flexibility for complex data modalities. Flow matching, therefore, enables exact density evaluation, fast training, and seamless scalability to large architectures--making it ideal for SBI. We show that FMPE achieves competitive performance on an established SBI benchmark, and then demonstrate its improved scalability on a challenging scientific problem: for gravitational-wave inference, FMPE outperforms methods based on comparable discrete flows, reducing training time by 30% with substantially improved accuracy. Our work underscores the potential of FMPE to enhance performance in challenging inference scenarios, thereby paving the way for more advanced applications to scientific problems.
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Authors: Richard Gao, Michael Deistler, Jakob H. Macke
Date: 2023-05-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Inference

Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be overly restrictive. Generalized Bayesian Inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data. However, GBI methods generally require running multiple simulations to estimate the cost function at each parameter value during inference, making the approach computationally infeasible for even moderately complex simulators. Here, we propose amortized cost estimation (ACE) for GBI to address this challenge: We train a neural network to approximate the cost function, which we define as the expected distance between simulations produced by a parameter and observed data. The trained network can then be used with MCMC to infer GBI posteriors for any observation without running additional simulations. We show that, on several benchmark tasks, ACE accurately predicts cost and provides predictive simulations that are closer to synthetic observations than other SBI methods, especially for misspecified simulators. Finally, we apply ACE to infer parameters of the Hodgkin-Huxley model given real intracellular recordings from the Allen Cell Types Database. ACE identifies better data-matching parameters while being an order of magnitude more simulation-efficient than a standard SBI method. In summary, ACE combines the strengths of SBI methods and GBI to perform robust and simulation-amortized inference for scientific simulators.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
Authors: Weijia Wu, Yuzhong Zhao, Hao Chen, YuChao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, Chunhua Shen
Date: 2023-08-11 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Dataset Generation, Decoder, Depth Estimation, Domain Generalization, Image Generation, Instance Segmentation, Segmentation, Semantic Segmentation, Zero Shot Segmentation

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectively
Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
Authors: Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E. Gonzalez, Trevor Darrell
Date: 2023-05-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Data Augmentation, Diversity, Domain Generalization, Image Augmentation

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
Authors: Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu
Date: 2023-02-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Classification

In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fr\'echet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256x256. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g., <0.1%) and generative augmentation remains viable for semi-supervised classification. Our code is available at https://github.com/ML-GSAI/DPT.
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
Authors: Yiren Jian, Chongyang Gao, Soroush Vosoughi
Date: 2023-07-13 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image to text

We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training. The current paradigm uses visual features as prompts to guide language models, with a focus on determining the most relevant visual features for corresponding text. Our approach diverges by concentrating on the language component, specifically identifying the optimal prompts to align with visual features. We introduce the Prompt-Transformer (P-Former), a model that predicts these ideal prompts, which is trained exclusively on linguistic data, bypassing the need for image-text pairings. This strategy subtly bifurcates the end-to-end VL training process into an additional, separate stage. Our experiments reveal that our framework significantly enhances the performance of a robust image-to-text baseline (BLIP-2), and effectively narrows the performance gap between models trained with either 4M or 129M image-text pairs. Importantly, our framework is modality-agnostic and flexible in terms of architectural design, as validated by its successful application in a video learning task using varied base modules. The code will be made available at https://github.com/yiren-jian/BLIText.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Authors: Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su
Date: 2023-05-18 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 3D Classification, 3D Shape Representation, Contrastive Learning, Image Generation, Zero-shot 3D classification, Zero-shot 3D Point Cloud Classification, Zero-Shot Transfer 3D Point Cloud Classification

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Learning Type Annotation: Is Big Data Enough?
Authors: Kevin Jesse, Premkumar T. Devanbu, Toufique Ahmed
Date: 2021-08-18 00:00:00
Proceeding: ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2021 8
Tasks: Inductive Bias

TypeScript is a widely used optionally-typed language where developers can adopt “pay as you go” typing: they can add types as
desired, and benefit from static typing. The “type annotation tax”
or manual effort required to annotate new or existing TypeScript
can be reduced by a variety of automatic methods. Probabilistic
machine-learning (ML) approaches work quite well. ML approaches
use different inductive biases, ranging from simple token sequences
to complex graphical neural network (GNN) models capturing syntax and semantic relations. More sophisticated inductive biases are
hand-engineered to exploit the formal nature of software. Rather
than deploying fancy inductive biases for code, can we just use “big
data” to learn natural patterns relevant to typing? We find evidence
suggesting that this is the case. We present TypeBert, demonstrating that even with simple token-sequence inductive bias used in
BERT-style models and enough data, type-annotation performance
of the most sophisticated models can be surpassed.
ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference
Authors: Kevin Jesse, Premkumar T. Devanbu
Date: 2022-10-17 00:00:00
Proceeding: International Conference on Mining Software Repositories 2022 10
Tasks: Type prediction

In this paper, we present ManyTypes4TypeScript, a very large
corpus for training and evaluating machine-learning models for
sequence-based type inference in TypeScript. The dataset includes
over 9 million type annotations, across 13,953 projects and 539,571
files. The dataset is approximately 10x larger than analogous type
inference datasets for Python, and is the largest available for TypeScript. We also provide API access to the dataset, which can be
integrated into any tokenizer and used with any state-of-the-art
sequence-based model. Finally, we provide analysis and performance results for state-of-the-art code-specific models, for baselining. ManyTypes4TypeScript is available on Huggingface, Zenodo,
and CodeXGLUE.
Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis
Authors: Ru Zhang; Feng Zhu; Jianyi Liu; Gongshen Liu
Date: 2019-08-22 00:00:00
Proceeding: journal 2019 8
Tasks: Data Augmentation, Steganalysis

For steganalysis, many studies showed that convolutional neural network (CNN) has better performances than the two-part structure of traditional machine learning methods. Existing CNN architectures use various tricks to improve the performance of steganalysis, such as fixed convolutional kernels, the absolute value layer, data augmentation and the domain knowledge. However, some designing of the network structure were not extensively studied so far, such as different convolutions (inception, xception, etc.) and variety ways of pooling(spatial pyramid pooling, etc.). In this paper, we focus on designing a new CNN network structure to improve detection accuracy of spatial-domain steganography. First, we use 3×3 kernels instead of the traditional 5×5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize channel correlation of the residuals, compress the image content and increase the signal-to-noise ratio (between the stego signal and the image signal). Then, we use spatial pyramid pooling (SPP) to aggregate the local features and enhance the representation ability of features by multi-level pooling. Finally, data augmentation is adopted to further improve network performance. The experimental results show that the proposed CNN structure is significantly better than other five methods such as SRM, Ye-Net, Xu-Net, Yedroudj-Net and SRNet, when it is used to detect three spatial algorithms such as WOW, S-UNIWARD and HILL with a wide variety of datasets and payloads.
Structural Design of Convolutional Neural Networks for Steganalysis
Authors: Guanshuo Xu; Han-Zhou Wu; Yun-Qing Shi
Date: 2016-03-30 00:00:00
Proceeding: journal 2016 3
Tasks: Steganalysis

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.
Deep learning hierarchical representations for image steganalysis
Authors: Jian Ye; Jiangqun Ni; Yang Yi
Date: 2016-06-01 00:00:00
Proceeding: journal 2016 6
Tasks: Binary Classification, Deep Learning, Steganalysis

Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i.e., residual computation, feature extraction, and binary classification. In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images. The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks. Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively. To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model. Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel. Three state-of-the-art steganographic algorithms in spatial domain, e.g., WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model. Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloads.
Fast and Effective Global Covariance Pooling Network for Image Steganalysis
Authors: Xiaoqing Deng, Bolin Chen, Weiqi Luo, Da Luo
Date: 2019-07-02 00:00:00
Proceeding: journal 2019 7
Tasks: Steganalysis

Recently, deep learning based methods have achieved superior performance compared to conventional methods based on hand-crafted features in image steganalysis. However, most modern methods are usually quite time consuming. For instance, it takes over 3 days to train a state-of-the-art neural network, i.e. SRNet [3] in our experiments. In this paper, therefore, we propose a fast yet very effective convolutional neural network (CNN) for image steganalysis in spatial domain. To make a good tradeoff between training time and performance, we carefully design the architecture of the proposed network according to our extensive experiments. In addition, we first introduce the global covariance pooling into steganalysis to exploit the second-order statistic of high-level features for further improving the performance. Experimental results show that the proposed network can outperform the current best one, while its training time is significantly reduced.