pwc-archive/papers-with-abstracts huggingface dataset:
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Authors: Fan Dong, Ali Abbasi, Henry Leung, Xin Wang, Jiayu Zhou, Steve Drew
Date: 2023-05-24 00:00:00
Tasks: Diversity, Federated Learning, Privacy Preserving

Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has extensively studied the optimization of the learning process, computing efficiency, and communication overhead. An important yet often overlooked aspect is that participants contribute predictive knowledge with varying diversity of knowledge, affecting the quality of the learned federated models. In this paper, we propose a novel approach to address this issue by introducing a Weighted Averaging and Client Selection (WeiAvgCS) framework that emphasizes updates from high-diversity clients and diminishes the influence of those from low-diversity clients. Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients. As such, we introduce an estimation for the diversity using a projection-based method. Extensive experiments have been performed to show WeiAvgCS's effectiveness. WeiAvgCS could converge 46% faster on FashionMNIST and 38% faster on CIFAR10 than its benchmarks on average in our experiments.
Lexinvariant Language Models
Date: 2023-05-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Language Modeling, Language Modelling

Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determined and even redefined by their structural role in a long context. In this paper, we ask: is it possible for a language model to be performant without \emph{any} fixed token embeddings? Such a language model would have to rely entirely on the co-occurence and repetition of tokens in the context rather than the \textit{a priori} identity of any token. To answer this, we study \textit{lexinvariant}language models that are invariant to lexical symbols and therefore do not need fixed token embeddings in practice. First, we prove that we can construct a lexinvariant LM to converge to the true language model at a uniform rate that is polynomial in terms of the context length, with a constant factor that is sublinear in the vocabulary size. Second, to build a lexinvariant LM, we simply encode tokens using random Gaussian vectors, such that each token maps to the same representation within each sequence but different representations across sequences. Empirically, we demonstrate that it can indeed attain perplexity comparable to that of a standard language model, given a sufficiently long context. We further explore two properties of the lexinvariant language models: First, given text generated from a substitution cipher of English, it implicitly implements Bayesian in-context deciphering and infers the mapping to the underlying real tokens with high accuracy. Second, it has on average 4X better accuracy over synthetic in-context reasoning tasks. Finally, we discuss regularizing standard language models towards lexinvariance and potential practical applications.
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Authors: Melvin Wong, Yew-Soon Ong, Abhishek Gupta, Kavitesh K. Bali, Caishun Chen
Date: 2023-05-24 00:00:00

Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fail to connect the generated outputs and desired target concepts/preferences implied by the prompts. Current research addressing this limitation has largely focused on enhancing the prompts before output generation or improving the model's performance up front. In contrast, this paper conceptualizes prompt evolution, imparting evolutionary selection pressure and variation during the generative process to produce multiple outputs that satisfy the target concepts/preferences better. We propose a multi-objective instantiation of this broader idea that uses a multi-label image classifier-guided approach. The predicted labels from the classifiers serve as multiple objectives to optimize, with the aim of producing diversified images that meet user preferences. A novelty of our evolutionary algorithm is that the pre-trained generative model gives us implicit mutation operations, leveraging the model's stochastic generative capability to automate the creation of Pareto-optimized images more faithful to user preferences.
Artificial Intelligence-Based Methods for Precision Medicine: Diabetes Risk Prediction
Authors: Farida Mohsen, Hamada R. H. Al-Absi, Noha A. Yousri, Nady El Hajj, Zubair Shah
Date: 2023-05-24 00:00:00

The rising prevalence of type 2 diabetes mellitus (T2DM) necessitates the development of predictive models for T2DM risk assessment. Artificial intelligence (AI) models are being extensively used for this purpose, but a comprehensive review of their advancements and challenges is lacking. This scoping review analyzes existing literature on AI-based models for T2DM risk prediction. Forty studies were included, mainly published in the past four years. Traditional machine learning models were more prevalent than deep learning models. Electronic health records were the most commonly used data source. Unimodal AI models relying on EHR data were prominent, while only a few utilized multimodal models. Both unimodal and multimodal models showed promising performance, with the latter outperforming the former. Internal validation was common, while external validation was limited. Interpretability methods were reported in half of the studies. Few studies reported novel biomarkers, and open-source code availability was limited. This review provides insights into the current state and limitations of AI-based T2DM risk prediction models and highlights challenges for their development and clinical implementation.
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
Authors: Chongjian Yue, Xinrun Xu, Xiaojun Ma, Lun Du, Hengyu Liu, Zhiming Ding, Yanbing Jiang, Shi Han, Dongmei Zhang
Date: 2023-05-24 00:00:00
Tasks: Retrieval

Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains underexplored. In this research, we specialize in harnessing the potential of LLMs to comprehend critical information from financial reports, which are hybrid long-documents. We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports. To evaluate AFIE, we develop a Financial Reports Numerical Extraction (FINE) dataset and conduct an extensive experimental analysis. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively, compared to a naive method. These results suggest that the AFIE framework offers accuracy for automated numerical extraction from complex, hybrid documents.
A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem
Authors: Ciprian-Octavian Truică, Neculai-Ovidiu Istrate, Elena-Simona Apostol
Date: 2023-05-24 00:00:00

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.
TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification
Authors: Mohsen Pourvali, Yao Meng, Chen Sheng, Yangzhou Du
Date: 2023-05-24 00:00:00
Tasks: Multi-class Classification

In this paper, we investigate the effectiveness of integrating a hierarchical taxonomy of labels as prior knowledge into the learning algorithm of a flat classifier. We introduce two methods to integrate the hierarchical taxonomy as an explicit regularizer into the loss function of learning algorithms. By reasoning on a hierarchical taxonomy, a neural network alleviates its output distributions over the classes, allowing conditioning on upper concepts for a minority class. We limit ourselves to the flat classification task and provide our experimental results on two industrial in-house datasets and two public benchmarks, RCV1 and Amazon product reviews. Our obtained results show the significant effect of a taxonomy in increasing the performance of a learner in semisupervised multi-class classification and the considerable results obtained in a fully supervised fashion.
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
Authors: Xinyue Wei, Fanbo Xiang, Sai Bi, Anpei Chen, Kalyan Sunkavalli, Zexiang Xu, Hao Su
Date: 2023-05-26 00:00:00

We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend to generate noisy meshes with poor topology. Differentiable rasterization-based methods can generate high-quality meshes but are sensitive to initialization. Our method combines the benefits of both worlds; we take the geometry initialization obtained from neural volumetric fields, and further optimize the geometry as well as a compact neural texture representation with differentiable rasterizers. Through extensive experiments, we demonstrate that our method can generate accurate mesh reconstructions with faithful appearance that are comparable to previous volume rendering methods while being an order of magnitude faster in rendering. We also show that our generated mesh and neural texture reconstruction is compatible with existing graphics pipelines and enables downstream 3D applications such as simulation. Project page: https://sarahweiii.github.io/neumanifold/
Manifold Regularization for Memory-Efficient Training of Deep Neural Networks
Authors: Shadi Sartipi, Edgar A. Bernal
Date: 2023-05-26 00:00:00
Tasks: Inductive Bias

One of the prevailing trends in the machine- and deep-learning community is to gravitate towards the use of increasingly larger models in order to keep pushing the state-of-the-art performance envelope. This tendency makes access to the associated technologies more difficult for the average practitioner and runs contrary to the desire to democratize knowledge production in the field. In this paper, we propose a framework for achieving improved memory efficiency in the process of learning traditional neural networks by leveraging inductive-bias-driven network design principles and layer-wise manifold-oriented regularization objectives. Use of the framework results in improved absolute performance and empirical generalization error relative to traditional learning techniques. We provide empirical validation of the framework, including qualitative and quantitative evidence of its effectiveness on two standard image datasets, namely CIFAR-10 and CIFAR-100. The proposed framework can be seamlessly combined with existing network compression methods for further memory savings.
Random-Access Neural Compression of Material Textures
Authors: Karthik Vaidyanathan, Marco Salvi, Bartlomiej Wronski, Tomas Akenine-Möller, Pontus Ebelin, Aaron Lefohn
Date: 2023-05-26 00:00:00
Tasks: Image Compression

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression technique specifically designed for material textures. We unlock two more levels of detail, i.e., 16x more texels, using low bitrate compression, with image quality that is better than advanced image compression techniques, such as AVIF and JPEG XL. At the same time, our method allows on-demand, real-time decompression with random access similar to block texture compression on GPUs, enabling compression on disk and memory. The key idea behind our approach is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them. Finally, we use a custom training implementation to achieve practical compression speeds, whose performance surpasses that of general frameworks, like PyTorch, by an order of magnitude.
GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation
Authors: Jingyang Huo, Qiang Sun, Boyan Jiang, Haitao Lin, Yanwei Fu
Date: 2023-05-26 00:00:00
Proceeding: CVPR 2023 1
Tasks: Vision and Language Navigation

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.
ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond
Authors: Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li, Jun Zhu
Date: 2023-05-26 00:00:00
Tasks: Text-to-Video Editing, Video Editing

This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions (such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating Low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing (e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous works achieved. The code is available at \href{https://github.com/thu-ml/controlvideo}{https://github.com/thu-ml/controlvideo} and the visualization results are available at \href{https://drive.google.com/file/d/1wEgc2io3UwmoC5vTPbkccFvTkwVqsZlK/view?usp=drive_link}{HERE}.
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Authors: Tanveer Hannan, Rajat Koner, Maximilian Bernhard, Suprosanna Shit, Bjoern Menze, Volker Tresp, Matthias Schubert, Thomas Seidl
Date: 2023-05-26 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation, Video Instance Segmentation

Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of representation and noise accumulation of the online methods, especially during occlusion and abrupt changes, pose substantial challenges. Transformer-based query propagation provides promising directions at the cost of quadratic memory attention. However, they are susceptible to the degradation of instance features due to the above-mentioned challenges and suffer from cascading effects. The detection and rectification of such errors remain largely underexplored. To this end, we introduce \textbf{GRAtt-VIS}, \textbf{G}ated \textbf{R}esidual \textbf{Att}ention for \textbf{V}ideo \textbf{I}nstance \textbf{S}egmentation. Firstly, we leverage a Gumbel-Softmax-based gate to detect possible errors in the current frame. Next, based on the gate activation, we rectify degraded features from its past representation. Such a residual configuration alleviates the need for dedicated memory and provides a continuous stream of relevant instance features. Secondly, we propose a novel inter-instance interaction using gate activation as a mask for self-attention. This masking strategy dynamically restricts the unrepresentative instance queries in the self-attention and preserves vital information for long-term tracking. We refer to this novel combination of Gated Residual Connection and Masked Self-Attention as \textbf{GRAtt} block, which can easily be integrated into the existing propagation-based framework. Further, GRAtt blocks significantly reduce the attention overhead and simplify dynamic temporal modeling. GRAtt-VIS achieves state-of-the-art performance on YouTube-VIS and the highly challenging OVIS dataset, significantly improving over previous methods. Code is available at \url{https://github.com/Tanveer81/GRAttVIS}.
SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch
Authors: Zhenchao Jin
Date: 2023-05-26 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper presents SSSegmenation, which is an open source supervised semantic image segmentation toolbox based on PyTorch. The design of this toolbox is motivated by MMSegmentation while it is easier to use because of fewer dependencies and achieves superior segmentation performance under a comparable training and testing setup. Moreover, the toolbox also provides plenty of trained weights for popular and contemporary semantic segmentation methods, including Deeplab, PSPNet, OCRNet, MaskFormer, \emph{etc}. We expect that this toolbox can contribute to the future development of semantic segmentation. Codes and model zoos are available at \href{https://github.com/SegmentationBLWX/sssegmentation/}{SSSegmenation}.
Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation
Authors: Peidi Xu, Olga Sosnovtseva, Charlotte Mehlin Sørensen, Kenny Erleben, Sune Darkner
Date: 2023-05-26 00:00:00
Tasks: Domain Adaptation

Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making more small vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a large amount of labeled training data. However, voxel-wise labeling in micro-CT scans is extremely time-consuming given the huge volume sizes. To mitigate the problem, we simulate synthetic renal vascular trees physiologically while generating corresponding scans of the simulated trees by training a generative model on unlabeled scans. This enables the generative model to learn the mapping implicitly without the need for explicit functions to emulate the image acquisition process. We further propose an additional segmentation branch over the generative model trained on the generated scans. We demonstrate that the model can directly segment blood vessels on real scans and validate our method on both 3D micro-CT scans of rat kidneys and a proof-of-concept experiment on 2D retinal images. Code and 3D results are available at https://github.com/miccai2023anony/RenalVesselSeg
Intrinsic Self-Supervision for Data Quality Audits
Authors: Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Labelling Consortium, Matthew Groh, Alexander A. Navarini, Marc Pouly
Date: 2023-05-26 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance. In this paper, we revisit the task of data cleaning and formalize it as either a ranking problem, which significantly reduces human inspection effort, or a scoring problem, which allows for automated decisions based on score distributions. We find that a specific combination of context-aware self-supervised representation learning and distance-based indicators is effective in finding issues without annotation biases. This methodology, which we call SelfClean, surpasses state-of-the-art performance in detecting off-topic images, near duplicates, and label errors within widely-used image datasets, such as ImageNet-1k, Food-101N, and STL-10, both for synthetic issues and real contamination. We apply the detailed method to multiple image benchmarks, identify up to 16% of issues, and confirm an improvement in evaluation reliability upon cleaning. The official implementation can be found at: https://github.com/Digital-Dermatology/SelfClean.