categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.15967 | null | null | http://arxiv.org/pdf/2404.15967v2 | 2024-04-25T17:13:45Z | 2024-04-24T16:38:15Z | Interpretable Clustering with the Distinguishability Criterion | Cluster analysis is a popular unsupervised learning tool used in many disciplines to identify heterogeneous sub-populations within a sample. However, validating cluster analysis results and determining the number of clusters in a data set remains an outstanding problem. In this work, we present a global criterion called the Distinguishability criterion to quantify the separability of identified clusters and validate inferred cluster configurations. Our computational implementation of the Distinguishability criterion corresponds to the Bayes risk of a randomized classifier under the 0-1 loss. We propose a combined loss function-based computational framework that integrates the Distinguishability criterion with many commonly used clustering procedures, such as hierarchical clustering, k-means, and finite mixture models. We present these new algorithms as well as the results from comprehensive data analysis based on simulation studies and real data applications. | [
"['Ali Turfah' 'Xiaoquan Wen']"
]
|
null | null | 2404.15979 | null | null | http://arxiv.org/pdf/2404.15979v1 | 2024-04-24T16:54:39Z | 2024-04-24T16:54:39Z | On the Fourier analysis in the SO(3) space : EquiLoPO Network | Analyzing volumetric data with rotational invariance or equivariance is an active topic in current research. Existing deep-learning approaches utilize either group convolutional networks limited to discrete rotations or steerable convolutional networks with constrained filter structures. This work proposes a novel equivariant neural network architecture that achieves analytical Equivariance to Local Pattern Orientation on the continuous SO(3) group while allowing unconstrained trainable filters - EquiLoPO Network. Our key innovations are a group convolutional operation leveraging irreducible representations as the Fourier basis and a local activation function in the SO(3) space that provides a well-defined mapping from input to output functions, preserving equivariance. By integrating these operations into a ResNet-style architecture, we propose a model that overcomes the limitations of prior methods. A comprehensive evaluation on diverse 3D medical imaging datasets from MedMNIST3D demonstrates the effectiveness of our approach, which consistently outperforms state of the art. This work suggests the benefits of true rotational equivariance on SO(3) and flexible unconstrained filters enabled by the local activation function, providing a flexible framework for equivariant deep learning on volumetric data with potential applications across domains. Our code is publicly available at url{https://gricad-gitlab.univ-grenoble-alpes.fr/GruLab/ILPO/-/tree/main/EquiLoPO}. | [
"['Dmitrii Zhemchuzhnikov' 'Sergei Grudinin']"
]
|
null | null | 2404.15993 | null | null | http://arxiv.org/pdf/2404.15993v3 | 2024-06-29T02:58:21Z | 2024-04-24T17:10:35Z | Uncertainty Estimation and Quantification for LLMs: A Simple Supervised
Approach | In this paper, we study the problem of uncertainty estimation and calibration for LLMs. We first formulate the uncertainty estimation problem for LLMs and then propose a supervised approach that takes advantage of the labeled datasets and estimates the uncertainty of the LLMs' responses. Based on the formulation, we illustrate the difference between the uncertainty estimation for LLMs and that for standard ML models and explain why the hidden neurons of the LLMs may contain uncertainty information. Our designed approach demonstrates the benefits of utilizing hidden activations to enhance uncertainty estimation across various tasks and shows robust transferability in out-of-distribution settings. We distinguish the uncertainty estimation task from the uncertainty calibration task and show that a better uncertainty estimation mode leads to a better calibration performance. Furthermore, our method is easy to implement and adaptable to different levels of model accessibility including black box, grey box, and white box. | [
"['Linyu Liu' 'Yu Pan' 'Xiaocheng Li' 'Guanting Chen']"
]
|
null | null | 2404.15999 | null | null | http://arxiv.org/pdf/2404.15999v1 | 2024-04-24T17:26:17Z | 2024-04-24T17:26:17Z | BeSound: Bluetooth-Based Position Estimation Enhancing with
Cross-Modality Distillation | Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based methods, ensures accurate monitoring. However, concerns about worker privacy and technology protection make it necessary to explore alternative approaches. We propose a non-visual, scalable solution using Bluetooth Low Energy (BLE) and ultrasound coordinates. BLE position estimation offers a very low-power and cost-effective solution, as the technology is available on smartphones and is scalable due to the large number of smartphone users, facilitating worker localization and safety protocol transmission. Ultrasound signals provide faster response times and higher accuracy but require custom hardware, increasing costs. To combine the benefits of both modalities, we employ knowledge distillation (KD) from ultrasound signals to BLE RSSI data. Once the student model is trained, the model only takes as inputs the BLE-RSSI data for inference, retaining the advantages of ubiquity and low cost of BLE RSSI. We tested our approach using data from an experiment with twelve participants in a smart factory test bed environment. We obtained an increase of 11.79% in the F1-score compared to the baseline (target model without KD and trained with BLE-RSSI data only). | [
"['Hymalai Bello' 'Sungho Suh' 'Bo Zhou' 'Paul Lukowicz']"
]
|
null | null | 2404.16000 | null | null | http://arxiv.org/pdf/2404.16000v1 | 2024-04-24T17:27:57Z | 2024-04-24T17:27:57Z | A comprehensive and easy-to-use multi-domain multi-task medical imaging
meta-dataset (MedIMeta) | While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often the scarcity of large, diverse, and well-annotated datasets. Medical images vary in format, size, and other parameters and therefore require extensive preprocessing and standardization, for usage in machine learning. Addressing these challenges, we introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, all of which are standardized to the same format and readily usable in PyTorch or other ML frameworks. We perform a technical validation of MedIMeta, demonstrating its utility through fully supervised and cross-domain few-shot learning baselines. | [
"['Stefano Woerner' 'Arthur Jaques' 'Christian F. Baumgartner']"
]
|
null | null | 2404.16005 | null | null | http://arxiv.org/abs/2404.16005v1 | 2024-04-24T17:35:29Z | 2024-04-24T17:35:29Z | Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition | Combining different sensing modalities with multiple positions helps form a unified perception and understanding of complex situations such as human behavior. Hence, human activity recognition (HAR) benefits from combining redundant and complementary information (Unimodal/Multimodal). Even so, it is not an easy task. It requires a multidisciplinary approach, including expertise in sensor technologies, signal processing, data fusion algorithms, and domain-specific knowledge. This Ph.D. work employs sensing modalities such as inertial, pressure (audio and atmospheric pressure), and textile capacitive sensing for HAR. The scenarios explored are gesture and hand position tracking, facial and head pattern recognition, and body posture and gesture recognition. The selected wearable devices and sensing modalities are fully integrated with machine learning-based algorithms, some of which are implemented in the embedded device, on the edge, and tested in real-time. | [
"['Hymalai Bello']"
]
|
null | null | 2404.16014 | null | null | http://arxiv.org/pdf/2404.16014v2 | 2024-04-30T17:54:04Z | 2024-04-24T17:47:22Z | Improving Dictionary Learning with Gated Sparse Autoencoders | Recent work has found that sparse autoencoders (SAEs) are an effective technique for unsupervised discovery of interpretable features in language models' (LMs) activations, by finding sparse, linear reconstructions of LM activations. We introduce the Gated Sparse Autoencoder (Gated SAE), which achieves a Pareto improvement over training with prevailing methods. In SAEs, the L1 penalty used to encourage sparsity introduces many undesirable biases, such as shrinkage -- systematic underestimation of feature activations. The key insight of Gated SAEs is to separate the functionality of (a) determining which directions to use and (b) estimating the magnitudes of those directions: this enables us to apply the L1 penalty only to the former, limiting the scope of undesirable side effects. Through training SAEs on LMs of up to 7B parameters we find that, in typical hyper-parameter ranges, Gated SAEs solve shrinkage, are similarly interpretable, and require half as many firing features to achieve comparable reconstruction fidelity. | [
"['Senthooran Rajamanoharan' 'Arthur Conmy' 'Lewis Smith' 'Tom Lieberum'\n 'Vikrant Varma' 'János Kramár' 'Rohin Shah' 'Neel Nanda']"
]
|
null | null | 2404.16017 | null | null | http://arxiv.org/pdf/2404.16017v2 | 2024-05-21T00:49:53Z | 2024-04-24T17:50:37Z | RetinaRegNet: A Versatile Approach for Retinal Image Registration | We introduce the RetinaRegNet model, which can achieve state-of-the-art performance across various retinal image registration tasks. RetinaRegNet does not require training on any retinal images. It begins by establishing point correspondences between two retinal images using image features derived from diffusion models. This process involves the selection of feature points from the moving image using the SIFT algorithm alongside random point sampling. For each selected feature point, a 2D correlation map is computed by assessing the similarity between the feature vector at that point and the feature vectors of all pixels in the fixed image. The pixel with the highest similarity score in the correlation map corresponds to the feature point in the moving image. To remove outliers in the estimated point correspondences, we first applied an inverse consistency constraint, followed by a transformation-based outlier detector. This method proved to outperform the widely used random sample consensus (RANSAC) outlier detector by a significant margin. To handle large deformations, we utilized a two-stage image registration framework. A homography transformation was used in the first stage and a more accurate third-order polynomial transformation was used in the second stage. The model's effectiveness was demonstrated across three retinal image datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. RetinaRegNet outperformed current state-of-the-art methods in all three datasets. It was especially effective for registering image pairs with large displacement and scaling deformations. This innovation holds promise for various applications in retinal image analysis. Our code is publicly available at https://github.com/mirthAI/RetinaRegNet . | [
"['Vishal Balaji Sivaraman' 'Muhammad Imran' 'Qingyue Wei'\n 'Preethika Muralidharan' 'Michelle R. Tamplin' 'Isabella M . Grumbach'\n 'Randy H. Kardon' 'Jui-Kai Wang' 'Yuyin Zhou' 'Wei Shao']"
]
|
null | null | 2404.16023 | null | null | http://arxiv.org/pdf/2404.16023v1 | 2024-04-24T17:55:47Z | 2024-04-24T17:55:47Z | Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture
Regression | Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation. Traditional CF models, though simple, often lack generalization capabilities, while many data-driven methods, despite their robustness, operate as "black boxes" with limited interpretability. To bridge this gap, this work introduces a Bayesian Matrix Normal Mixture Regression (MNMR) model that simultaneously captures feature correlations and temporal dynamics inherent in CF behaviors. This approach is distinguished by its separate learning of row and column covariance matrices within the model framework, offering an insightful perspective into the human driver decision-making processes. Through extensive experiments, we assess the model's performance across various historical steps of inputs, predictive steps of outputs, and model complexities. The results consistently demonstrate our model's adeptness in effectively capturing the intricate correlations and temporal dynamics present during CF. A focused case study further illustrates the model's outperforming interpretability of identifying distinct operational conditions through the learned mean and covariance matrices. This not only underlines our model's effectiveness in understanding complex human driving behaviors in CF scenarios but also highlights its potential as a tool for enhancing the interpretability of CF behaviors in traffic simulations and autonomous driving systems. | [
"['Chengyuan Zhang' 'Kehua Chen' 'Meixin Zhu' 'Hai Yang' 'Lijun Sun']"
]
|
null | null | 2404.16030 | null | null | http://arxiv.org/pdf/2404.16030v1 | 2024-04-24T17:59:24Z | 2024-04-24T17:59:24Z | MoDE: CLIP Data Experts via Clustering | The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster, being less sensitive to false negative noises in other clusters. At inference time, we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely, the samples in one cluster should be semantically similar, but the number of data experts should still be reasonable for training and inference. As such, we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less ($<$35%) training cost. Meanwhile, MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available at https://github.com/facebookresearch/MetaCLIP/tree/main/mode. | [
"['Jiawei Ma' 'Po-Yao Huang' 'Saining Xie' 'Shang-Wen Li'\n 'Luke Zettlemoyer' 'Shih-Fu Chang' 'Wen-Tau Yih' 'Hu Xu']"
]
|
null | null | 2404.16032 | null | null | http://arxiv.org/pdf/2404.16032v1 | 2024-04-24T17:59:36Z | 2024-04-24T17:59:36Z | Studying Large Language Model Behaviors Under Realistic Knowledge
Conflicts | Retrieval-augmented generation (RAG) mitigates many problems of fully parametric language models, such as temporal degradation, hallucinations, and lack of grounding. In RAG, the model's knowledge can be updated from documents provided in context. This leads to cases of conflict between the model's parametric knowledge and the contextual information, where the model may not always update its knowledge. Previous work studied knowledge conflicts by creating synthetic documents that contradict the model's correct parametric answers. We present a framework for studying knowledge conflicts in a realistic setup. We update incorrect parametric knowledge using real conflicting documents. This reflects how knowledge conflicts arise in practice. In this realistic scenario, we find that knowledge updates fail less often than previously reported. In cases where the models still fail to update their answers, we find a parametric bias: the incorrect parametric answer appearing in context makes the knowledge update likelier to fail. These results suggest that the factual parametric knowledge of LLMs can negatively influence their reading abilities and behaviors. Our code is available at https://github.com/kortukov/realistic_knowledge_conflicts/. | [
"['Evgenii Kortukov' 'Alexander Rubinstein' 'Elisa Nguyen' 'Seong Joon Oh']"
]
|
null | null | 2404.16037 | null | null | http://arxiv.org/pdf/2404.16037v1 | 2024-01-26T12:41:57Z | 2024-01-26T12:41:57Z | VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse
Spatio-Temporal Meteorological Forecasting | Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention. Recent studies have highlighted the potential of spatio-temporal graph convolutional networks (ST-GCNs) in predicting numerical data from ground weather stations. However, as one of the highest fidelity and lowest latency data, the application of the vision data from satellites in ST-GCNs remains unexplored. There are few studies to demonstrate the effectiveness of combining the above multi-modal data for sparse meteorological forecasting. Towards this objective, we introduce Vision-Numerical Fusion Graph Convolutional Network (VN-Net), which mainly utilizes: 1) Numerical-GCN (N-GCN) to adaptively model the static and dynamic patterns of spatio-temporal numerical data; 2) Vision-LSTM Network (V-LSTM) to capture multi-scale joint channel and spatial features from time series satellite images; 4) a GCN-based decoder to generate hourly predictions of specified meteorological factors. As far as we know, VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse spatio-temporal meteorological forecasting. Our experiments on Weather2k dataset show VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting. Furthermore, we conduct interpretation analysis and design quantitative evaluation metrics to assess the impact of incorporating vision data. | [
"['Yutong Xiong' 'Xun Zhu' 'Ming Wu' 'Weiqing Li' 'Fanbin Mo'\n 'Chuang Zhang' 'Bin Zhang']"
]
|
null | null | 2404.16041 | null | null | http://arxiv.org/pdf/2404.16041v1 | 2024-04-01T17:27:58Z | 2024-04-01T17:27:58Z | Forklift: An Extensible Neural Lifter | The escalating demand to migrate legacy software across different Instruction Set Architectures (ISAs) has driven the development of assembly-to-assembly translators to map between their respective assembly languages. However, the development of these tools requires substantial engineering effort. State-of-the-art approaches use lifting, a technique where source assembly code is translated to an architecture-independent intermediate representation (IR) (for example, the LLVM IR) and use a pre-existing compiler to recompile the IR to the target ISA. However, the hand-written rules these lifters employ are sensitive to the particular compiler and optimization level used to generate the code and require significant engineering effort to support each new ISA. We propose Forklift, the first neural lifter that learns how to translate assembly to LLVM IR using a token-level encoder-decoder Transformer. We show how to incrementally add support to new ISAs by fine tuning the assembly encoder and freezing the IR decoder, improving the overall accuracy and efficiency. We collect millions of parallel LLVM IR, x86, ARM, and RISC-V programs across compilers and optimization levels to train Forklift and set up an input/output-based accuracy harness. We evaluate Forklift on two challenging benchmark suites and translate 2.5x more x86 programs than a state-of-the-art hand-written lifter and 4.4x more x86 programs than GPT-4 as well as enabling translation from new ISAs. | [
"['Jordi Armengol-Estapé' 'Rodrigo C. O. Rocha' 'Jackson Woodruff'\n 'Pasquale Minervini' \"Michael F. P. O'Boyle\"]"
]
|
null | null | 2404.16049 | null | null | http://arxiv.org/pdf/2404.16049v1 | 2024-04-09T14:08:23Z | 2024-04-09T14:08:23Z | Exploring the limitations of blood pressure estimation using the
photoplethysmography signal | Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. Photoplethysmography (PPG) presents a promising approach to this end. However, the precision of BP estimates derived from PPG signals has been the subject of ongoing debate, necessitating a comprehensive evaluation of their effectiveness and constraints. We developed a calibration-based Siamese ResNet model for BP estimation, using a signal input paired with a reference BP reading. We compared the use of normalized PPG (N-PPG) against the normalized Invasive Arterial Blood Pressure (N-IABP) signals as input. The N-IABP signals do not directly present systolic and diastolic values but theoretically provide a more accurate BP measure than PPG signals since it is a direct pressure sensor inside the body. Our strategy establishes a critical benchmark for PPG performance, realistically calibrating expectations for PPG's BP estimation capabilities. Nonetheless, we compared the performance of our models using different signal-filtering conditions to evaluate the impact of filtering on the results. We evaluated our method using the AAMI and the BHS standards employing the VitalDB dataset. The N-IABP signals meet with AAMI standards for both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), with errors of 1.29+-6.33mmHg for systolic pressure and 1.17+-5.78mmHg for systolic and diastolic pressure respectively for the raw N-IABP signal. In contrast, N-PPG signals, in their best setup, exhibited inferior performance than N-IABP, presenting 1.49+-11.82mmHg and 0.89+-7.27mmHg for systolic and diastolic pressure respectively. Our findings highlight the potential and limitations of employing PPG for BP estimation, showing that these signals contain information correlated to BP but may not be sufficient for predicting it accurately. | [
"['Felipe M. Dias' 'Diego A. C. Cardenas' 'Marcelo A. F. Toledo'\n 'Filipe A. C. Oliveira' 'Estela Ribeiro' 'Jose E. Krieger'\n 'Marco A. Gutierrez']"
]
|
null | null | 2404.16064 | null | null | http://arxiv.org/pdf/2404.16064v1 | 2024-04-18T21:01:27Z | 2024-04-18T21:01:27Z | Transparent AI: Developing an Explainable Interface for Predicting
Postoperative Complications | Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern. Existing artificial intelligence (AI) tools for risk surveillance and diagnosis often lack adequate interpretability, fairness, and reproducibility. To address this, we proposed an Explainable AI (XAI) framework designed to answer five critical questions: why, why not, how, what if, and what else, with the goal of enhancing the explainability and transparency of AI models. We incorporated various techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), counterfactual explanations, model cards, an interactive feature manipulation interface, and the identification of similar patients to address these questions. We showcased an XAI interface prototype that adheres to this framework for predicting major postoperative complications. This initial implementation has provided valuable insights into the vast explanatory potential of our XAI framework and represents an initial step towards its clinical adoption. | [
"['Yuanfang Ren' 'Chirayu Tripathi' 'Ziyuan Guan' 'Ruilin Zhu'\n 'Victoria Hougha' 'Yingbo Ma' 'Zhenhong Hu' 'Jeremy Balch'\n 'Tyler J. Loftus' 'Parisa Rashidi' 'Benjamin Shickel'\n 'Tezcan Ozrazgat-Baslanti' 'Azra Bihorac']"
]
|
null | null | 2404.16066 | null | null | http://arxiv.org/pdf/2404.16066v2 | 2024-06-23T14:39:13Z | 2024-04-20T16:36:28Z | Social Media Use is Predictable from App Sequences: Using LSTM and
Transformer Neural Networks to Model Habitual Behavior | The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use. We examine the performance of several modeling approaches, including (i) global models trained on the pooled data from all participants, (ii) idiographic person-specific models, and (iii) global models fine-tuned on person-specific data. Neither person-specific modeling nor fine-tuning on person-specific data substantially outperformed the global models, indicating that the global models were able to represent a variety of idiosyncratic behavioral patterns. Additionally, our analyses reveal that the person-level predictability of social media use is not substantially related to the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency. Implications for habit modeling and theoretical development are discussed. | [
"['Heinrich Peters' 'Joseph B. Bayer' 'Sandra C. Matz' 'Yikun Chi'\n 'Sumer S. Vaid' 'Gabriella M. Harari']"
]
|
null | null | 2404.16068 | null | null | http://arxiv.org/pdf/2404.16068v1 | 2024-04-22T07:21:27Z | 2024-04-22T07:21:27Z | SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense | While vertical thinking relies on logical and commonsense reasoning, lateral thinking requires systems to defy commonsense associations and overwrite them through unconventional thinking. Lateral thinking has been shown to be challenging for current models but has received little attention. A recent benchmark, BRAINTEASER, aims to evaluate current models' lateral thinking ability in a zero-shot setting. In this paper, we split the original benchmark to also support fine-tuning setting and present SemEval Task 9: BRAIN-TEASER(S), the first task at this competition designed to test the system's reasoning and lateral thinking ability. As a popular task, BRAINTEASER(S)'s two subtasks receive 483 team submissions from 182 participants during the competition. This paper provides a fine-grained system analysis of the competition results, together with a reflection on what this means for the ability of the systems to reason laterally. We hope that the BRAINTEASER(S) subtasks and findings in this paper can stimulate future work on lateral thinking and robust reasoning by computational models. | [
"['Yifan Jiang' 'Filip Ilievski' 'Kaixin Ma']"
]
|
null | null | 2404.16072 | null | null | http://arxiv.org/pdf/2404.16072v1 | 2024-04-23T20:10:27Z | 2024-04-23T20:10:27Z | Playing Board Games with the Predict Results of Beam Search Algorithm | This paper introduces a novel algorithm for two-player deterministic games with perfect information, which we call PROBS (Predict Results of Beam Search). Unlike existing methods that predominantly rely on Monte Carlo Tree Search (MCTS) for decision processes, our approach leverages a simpler beam search algorithm. We evaluate the performance of our algorithm across a selection of board games, where it consistently demonstrates an increased winning ratio against baseline opponents. A key result of this study is that the PROBS algorithm operates effectively, even when the beam search size is considerably smaller than the average number of turns in the game. | [
"['Sergey Pastukhov']"
]
|
null | null | 2404.16076 | null | null | http://arxiv.org/pdf/2404.16076v1 | 2024-04-24T05:05:58Z | 2024-04-24T05:05:58Z | Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection | Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual information and the propagation structure of events have been proposed. However, these methods overlook the importance of semantic evolvement information of event in propagation process, which is often challenging to be truly learned in supervised training paradigms and traditional rumor detection methods. To address this issue, we propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information through specific graph autoencoder and reconstruction strategies. By combining semantic evolvement information and propagation structure information, the model achieves a comprehensive understanding of event propagation and perform accurate and robust detection, while also detecting rumors earlier by capturing semantic evolvement information in the early stages. Moreover, in order to enhance the model's ability to learn the distinct patterns of rumors and non-rumors, we introduce a uniformity regularizer to further improve the model's performance. Experimental results on three public benchmark datasets confirm the superiority of our GARD method over the state-of-the-art approaches in both overall performance and early rumor detection. | [
"['Xiang Tao' 'Liang Wang' 'Qiang Liu' 'Shu Wu' 'Liang Wang']"
]
|
null | null | 2404.16077 | null | null | http://arxiv.org/pdf/2404.16077v1 | 2024-04-24T09:20:33Z | 2024-04-24T09:20:33Z | Supercompiler Code Optimization with Zero-Shot Reinforcement Learning | Effective code optimization in compilers plays a central role in computer and software engineering. While compilers can be made to automatically search the optimization space without the need for user interventions, this is not a standard practice since the search is slow and cumbersome. Here we present CodeZero, an artificial intelligence agent trained extensively on large data to produce effective optimization strategies instantly for each program in a single trial of the agent. To overcome the huge range of possible test programs, we prepare a large dataset of training programs that emphasize quality, naturalness, and diversity. To tackle the vast space of possible optimizations, we adapt deep reinforcement learning to train the agent in a sample-efficient manner through interacting with a world model of the compiler environment. Evaluation on both benchmark suites and production-level code optimization problems demonstrates our agent's supercompiler performances and zero-shot generalization abilities, outperforming built-in optimization options designed by compiler experts. Our methodology kindles the great potential of artificial intelligence for engineering and paves the way for scaling machine learning techniques in the realm of code optimization. | [
"['Jialong Wu' 'Chaoyi Deng' 'Jianmin Wang' 'Mingsheng Long']"
]
|
null | null | 2404.16078 | null | null | http://arxiv.org/pdf/2404.16078v2 | 2024-04-26T09:54:28Z | 2024-04-24T12:41:04Z | Learning World Models With Hierarchical Temporal Abstractions: A
Probabilistic Perspective | Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatio-temporal abstractions and scales using internal world models. Devising formalisms to develop such internal world models, which accurately reflect the causal hierarchies inherent in the dynamics of the real world, is a critical research challenge in the domains of artificial intelligence and machine learning. This thesis identifies several limitations with the prevalent use of state space models (SSMs) as internal world models and propose two new probabilistic formalisms namely Hidden-Parameter SSMs and Multi-Time Scale SSMs to address these drawbacks. The structure of graphical models in both formalisms facilitates scalable exact probabilistic inference using belief propagation, as well as end-to-end learning via backpropagation through time. This approach permits the development of scalable, adaptive hierarchical world models capable of representing nonstationary dynamics across multiple temporal abstractions and scales. Moreover, these probabilistic formalisms integrate the concept of uncertainty in world states, thus improving the system's capacity to emulate the stochastic nature of the real world and quantify the confidence in its predictions. The thesis also discuss how these formalisms are in line with related neuroscience literature on Bayesian brain hypothesis and predicitive processing. Our experiments on various real and simulated robots demonstrate that our formalisms can match and in many cases exceed the performance of contemporary transformer variants in making long-range future predictions. We conclude the thesis by reflecting on the limitations of our current models and suggesting directions for future research. | [
"['Vaisakh Shaj']"
]
|
null | null | 2404.16109 | null | null | http://arxiv.org/pdf/2404.16109v1 | 2024-04-24T18:04:50Z | 2024-04-24T18:04:50Z | zkLLM: Zero Knowledge Proofs for Large Language Models | The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage. | [
"['Haochen Sun' 'Jason Li' 'Hongyang Zhang']"
]
|
null | null | 2404.16112 | null | null | http://arxiv.org/pdf/2404.16112v1 | 2024-04-24T18:10:31Z | 2024-04-24T18:10:31Z | Mamba-360: Survey of State Space Models as Transformer Alternative for
Long Sequence Modelling: Methods, Applications, and Challenges | Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) have historically dominated sequence modeling tasks like Machine Translation, Named Entity Recognition (NER), etc. However, the advancement of transformers has led to a shift in this paradigm, given their superior performance. Yet, transformers suffer from $O(N^2)$ attention complexity and challenges in handling inductive bias. Several variations have been proposed to address these issues which use spectral networks or convolutions and have performed well on a range of tasks. However, they still have difficulty in dealing with long sequences. State Space Models(SSMs) have emerged as promising alternatives for sequence modeling paradigms in this context, especially with the advent of S4 and its variants, such as S4nd, Hippo, Hyena, Diagnol State Spaces (DSS), Gated State Spaces (GSS), Linear Recurrent Unit (LRU), Liquid-S4, Mamba, etc. In this survey, we categorize the foundational SSMs based on three paradigms namely, Gating architectures, Structural architectures, and Recurrent architectures. This survey also highlights diverse applications of SSMs across domains such as vision, video, audio, speech, language (especially long sequence modeling), medical (including genomics), chemical (like drug design), recommendation systems, and time series analysis, including tabular data. Moreover, we consolidate the performance of SSMs on benchmark datasets like Long Range Arena (LRA), WikiText, Glue, Pile, ImageNet, Kinetics-400, sstv2, as well as video datasets such as Breakfast, COIN, LVU, and various time series datasets. The project page for Mamba-360 work is available on this webpage.url{https://github.com/badripatro/mamba360}. | [
"['Badri Narayana Patro' 'Vijay Srinivas Agneeswaran']"
]
|
null | null | 2404.16115 | null | null | http://arxiv.org/pdf/2404.16115v1 | 2024-04-24T18:13:12Z | 2024-04-24T18:13:12Z | Online Personalizing White-box LLMs Generation with Neural Bandits | The advent of personalized content generation by LLMs presents a novel challenge: how to efficiently adapt text to meet individual preferences without the unsustainable demand of creating a unique model for each user. This study introduces an innovative online method that employs neural bandit algorithms to dynamically optimize soft instruction embeddings based on user feedback, enhancing the personalization of open-ended text generation by white-box LLMs. Through rigorous experimentation on various tasks, we demonstrate significant performance improvements over baseline strategies. NeuralTS, in particular, leads to substantial enhancements in personalized news headline generation, achieving up to a 62.9% improvement in terms of best ROUGE scores and up to 2.76% increase in LLM-agent evaluation against the baseline. | [
"['Zekai Chen' 'Weeden Daniel' 'Po-yu Chen' 'Francois Buet-Golfouse']"
]
|
null | null | 2404.16127 | null | null | http://arxiv.org/pdf/2404.16127v2 | 2024-05-24T16:43:16Z | 2024-04-24T18:31:48Z | Comparison of static and dynamic random forests models for EHR data in
the presence of competing risks: predicting central line-associated
bloodstream infection | Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of random forest (RF) models to predict the risk of central line-associated bloodstream infections (CLABSI) using different outcome operationalizations. We included data from 27478 admissions to the University Hospitals Leuven, covering 30862 catheter episodes (970 CLABSI, 1466 deaths and 28426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) outcomes to predict the 7-day CLABSI risk. We evaluated model performance across 100 train/test splits. Performance of binary, multinomial and competing risks models was similar: AUROC was 0.74 for baseline predictions, rose to 0.78 for predictions at day 5 in the catheter episode, and decreased thereafter. Survival models overestimated the risk of CLABSI (E:O ratios between 1.2 and 1.6), and had AUROCs about 0.01 lower than other models. Binary and multinomial models had lowest computation times. Models including multiple outcome events (multinomial and competing risks) display a different internal structure compared to binary and survival models. In the absence of censoring, complex modelling choices do not considerably improve the predictive performance compared to a binary model for CLABSI prediction in our studied settings. Survival models censoring the competing events at their time of occurrence should be avoided. | [
"['Elena Albu' 'Shan Gao' 'Pieter Stijnen' 'Frank Rademakers'\n 'Christel Janssens' 'Veerle Cossey' 'Yves Debaveye' 'Laure Wynants'\n 'Ben Van Calster']"
]
|
null | null | 2404.16131 | null | null | http://arxiv.org/pdf/2404.16131v1 | 2024-04-24T18:39:18Z | 2024-04-24T18:39:18Z | Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and
Better | Cluster deletion is an NP-hard graph clustering objective with applications in computational biology and social network analysis, where the goal is to delete a minimum number of edges to partition a graph into cliques. We first provide a tighter analysis of two previous approximation algorithms, improving their approximation guarantees from 4 to 3. Moreover, we show that both algorithms can be derandomized in a surprisingly simple way, by greedily taking a vertex of maximum degree in an auxiliary graph and forming a cluster around it. One of these algorithms relies on solving a linear program. Our final contribution is to design a new and purely combinatorial approach for doing so that is far more scalable in theory and practice. | [
"['Vicente Balmaseda' 'Ying Xu' 'Yixin Cao' 'Nate Veldt']"
]
|
null | null | 2404.16133 | null | null | http://arxiv.org/pdf/2404.16133v1 | 2024-04-24T18:40:45Z | 2024-04-24T18:40:45Z | Quantitative Characterization of Retinal Features in Translated OCTA | Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware. Methods: The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA). We then quantitatively characterize vascular features generated in TR-OCTAs with GT-OCTAs to assess the feasibility of using TR-OCTA for objective disease diagnosis. Result: TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed a better trend compared to density features which are affected by local vascular distortions. Conclusion: This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. Translation relevance: This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment. | [
"['Rashadul Hasan Badhon' 'Atalie Carina Thompson' 'Jennifer I. Lim'\n 'Theodore Leng' 'Minhaj Nur Alam']"
]
|
null | null | 2404.16134 | null | null | http://arxiv.org/abs/2404.16134v1 | 2024-04-24T18:45:50Z | 2024-04-24T18:45:50Z | Power Failure Cascade Prediction using Graph Neural Networks | We consider the problem of predicting power failure cascades due to branch failures. We propose a flow-free model based on graph neural networks that predicts grid states at every generation of a cascade process given an initial contingency and power injection values. We train the proposed model using a cascade sequence data pool generated from simulations. We then evaluate our model at various levels of granularity. We present several error metrics that gauge the model's ability to predict the failure size, the final grid state, and the failure time steps of each branch within the cascade. We benchmark the graph neural network model against influence models. We show that, in addition to being generic over randomly scaled power injection values, the graph neural network model outperforms multiple influence models that are built specifically for their corresponding loading profiles. Finally, we show that the proposed model reduces the computational time by almost two orders of magnitude. | [
"['Sathwik Chadaga' 'Xinyu Wu' 'Eytan Modiano']"
]
|
null | null | 2404.16137 | null | null | http://arxiv.org/pdf/2404.16137v1 | 2024-04-24T18:50:56Z | 2024-04-24T18:50:56Z | Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM | High peak-to-average power ratio (PAPR) is one of the main factors limiting cell coverage for cellular systems, especially in the uplink direction. Discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM) with spectrally-extended frequency-domain spectrum shaping (FDSS) is one of the efficient techniques deployed to lower the PAPR of the uplink waveforms. In this work, we propose a machine learning-based framework to determine the FDSS filter, optimizing a tradeoff between the symbol error rate (SER), the PAPR, and the spectral flatness requirements. Our end-to-end optimization framework considers multiple important design constraints, including the Nyquist zero-ISI (inter-symbol interference) condition. The numerical results show that learned FDSS filters lower the PAPR compared to conventional baselines, with minimal SER degradation. Tuning the parameters of the optimization also helps us understand the fundamental limitations and characteristics of the FDSS filters for PAPR reduction. | [
"['Fabrizio Carpi' 'Soheil Rostami' 'Joonyoung Cho' 'Siddharth Garg'\n 'Elza Erkip' 'Charlie Jianzhong Zhang']"
]
|
null | null | 2404.16141 | null | null | http://arxiv.org/pdf/2404.16141v1 | 2024-04-24T18:58:00Z | 2024-04-24T18:58:00Z | Machine-Learned Closure of URANS for Stably Stratified Turbulence:
Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series
Models | We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying cases. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the URANS equations. Rather than attempting to model each term separately, it is attractive to explore the capability of machine learning to model groups of terms, i.e., to directly model the force balances. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: Long Short-Term Memory (LSTM) and Neural Ordinary Differential Equation (NODE). Both models perform accurately and are numerically stable in a posteriori tests. Furthermore, we explore the data requirements of the ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of higher-dimensional complex SST flows. | [
"['Muralikrishnan Gopalakrishnan Meena' 'Demetri Liousas' 'Andrew D. Simin'\n 'Aditya Kashi' 'Wesley H. Brewer' 'James J. Riley'\n 'Stephen M. de Bruyn Kops']"
]
|
null | null | 2404.16154 | null | null | http://arxiv.org/pdf/2404.16154v1 | 2024-04-24T19:20:15Z | 2024-04-24T19:20:15Z | A Comparative Analysis of Adversarial Robustness for Quantum and
Classical Machine Learning Models | Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models, it is still largely unknown how to compare adversarial attacks on quantum versus classical models. In this paper, we show how to systematically investigate the similarities and differences in adversarial robustness of classical and quantum models using transfer attacks, perturbation patterns and Lipschitz bounds. More specifically, we focus on classification tasks on a handcrafted dataset that allows quantitative analysis for feature attribution. This enables us to get insight, both theoretically and experimentally, on the robustness of classification networks. We start by comparing typical QML model architectures such as amplitude and re-upload encoding circuits with variational parameters to a classical ConvNet architecture. Next, we introduce a classical approximation of QML circuits (originally obtained with Random Fourier Features sampling but adapted in this work to fit a trainable encoding) and evaluate this model, denoted Fourier network, in comparison to other architectures. Our findings show that this Fourier network can be seen as a "middle ground" on the quantum-classical boundary. While adversarial attacks successfully transfer across this boundary in both directions, we also show that regularization helps quantum networks to be more robust, which has direct impact on Lipschitz bounds and transfer attacks. | [
"['Maximilian Wendlinger' 'Kilian Tscharke' 'Pascal Debus']"
]
|
null | null | 2404.16155 | null | null | http://arxiv.org/pdf/2404.16155v1 | 2024-04-24T19:22:45Z | 2024-04-24T19:22:45Z | Does SAM dream of EIG? Characterizing Interactive Segmenter Performance
using Expected Information Gain | We introduce an assessment procedure for interactive segmentation models. Based on concepts from Bayesian Experimental Design, the procedure measures a model's understanding of point prompts and their correspondence with the desired segmentation mask. We show that Oracle Dice index measurements are insensitive or even misleading in measuring this property. We demonstrate the use of the proposed procedure on three interactive segmentation models and subsets of two large image segmentation datasets. | [
"['Kuan-I Chung' 'Daniel Moyer']"
]
|
null | null | 2404.16156 | null | null | http://arxiv.org/pdf/2404.16156v3 | 2024-05-15T20:55:24Z | 2024-04-24T19:24:53Z | Guardians of the Quantum GAN | Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and infer quantum machine learning models, the number of third-party vendors offering quantum hardware as a service is expected to rise. This expansion introduces the risk of untrusted vendors potentially stealing proprietary information from the quantum machine learning models. To address this concern we propose a novel watermarking technique that exploits the noise signature embedded during the training phase of qGANs as a non-invasive watermark. The watermark is identifiable in the images generated by the qGAN allowing us to trace the specific quantum hardware used during training hence providing strong proof of ownership. To further enhance the security robustness, we propose the training of qGANs on a sequence of multiple quantum hardware, embedding a complex watermark comprising the noise signatures of all the training hardware that is difficult for adversaries to replicate. We also develop a machine learning classifier to extract this watermark robustly, thereby identifying the training hardware (or the suite of hardware) from the images generated by the qGAN validating the authenticity of the model. We note that the watermark signature is robust against inferencing on hardware different than the hardware that was used for training. We obtain watermark extraction accuracy of 100% and ~90% for training the qGAN on individual and multiple quantum hardware setups (and inferencing on different hardware), respectively. Since parameter evolution during training is strongly modulated by quantum noise, the proposed watermark can be extended to other quantum machine learning models as well. | [
"['Archisman Ghosh' 'Debarshi Kundu' 'Avimita Chatterjee' 'Swaroop Ghosh']"
]
|
null | null | 2404.16158 | null | null | http://arxiv.org/pdf/2404.16158v1 | 2024-04-24T19:25:58Z | 2024-04-24T19:25:58Z | The Feasibility of Implementing Large-Scale Transformers on Multi-FPGA
Platforms | FPGAs are rarely mentioned when discussing the implementation of large machine learning applications, such as Large Language Models (LLMs), in the data center. There has been much evidence showing that single FPGAs can be competitive with GPUs in performance for some computations, especially for low latency, and often much more efficient when power is considered. This suggests that there is merit to exploring the use of multiple FPGAs for large machine learning applications. The challenge with using multiple FPGAs is that there is no commonly-accepted flow for developing and deploying multi-FPGA applications, i.e., there are no tools to describe a large application, map it to multiple FPGAs and then deploy the application on a multi-FPGA platform. In this paper, we explore the feasibility of implementing large transformers using multiple FPGAs by developing a scalable multi-FPGA platform and some tools to map large applications to the platform. We validate our approach by designing an efficient multi-FPGA version of the I-BERT transformer and implement one encoder using six FPGAs as a working proof-of-concept to show that our platform and tools work. Based on our proof-of-concept prototype and the estimations of performance using the latest FPGAs compared to GPUs, we conclude that there can be a place for FPGAs in the world of large machine learning applications. We demonstrate a promising first step that shows that with the right infrastructure and tools it is reasonable to continue to explore the possible benefits of using FPGAs for applications such as LLMs. | [
"['Yu Gao' 'Juan Camilo Vega' 'Paul Chow']"
]
|
null | null | 2404.16159 | null | null | http://arxiv.org/pdf/2404.16159v1 | 2024-04-24T19:30:03Z | 2024-04-24T19:30:03Z | AFU: Actor-Free critic Updates in off-policy RL for continuous control | This paper presents AFU, an off-policy deep RL algorithm addressing in a new way the challenging "max-Q problem" in Q-learning for continuous action spaces, with a solution based on regression and conditional gradient scaling. AFU has an actor but its critic updates are entirely independent from it. As a consequence, the actor can be chosen freely. In the initial version, AFU-alpha, we employ the same stochastic actor as in Soft Actor-Critic (SAC), but we then study a simple failure mode of SAC and show how AFU can be modified to make actor updates less likely to become trapped in local optima, resulting in a second version of the algorithm, AFU-beta. Experimental results demonstrate the sample efficiency of both versions of AFU, marking it as the first model-free off-policy algorithm competitive with state-of-the-art actor-critic methods while departing from the actor-critic perspective. | [
"['Nicolas Perrin-Gilbert']"
]
|
null | null | 2404.16164 | null | null | http://arxiv.org/pdf/2404.16164v1 | 2024-04-24T19:40:01Z | 2024-04-24T19:40:01Z | Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall | Large language models (LLMs) have shown remarkable performance on a variety of NLP tasks, and are being rapidly adopted in a wide range of use cases. It is therefore of vital importance to holistically evaluate the factuality of their generated outputs, as hallucinations remain a challenging issue. In this work, we focus on assessing LLMs' ability to recall factual knowledge learned from pretraining, and the factors that affect this ability. To that end, we construct FACT-BENCH, a representative benchmark covering 20 domains, 134 property types, 3 answer types, and different knowledge popularity levels. We benchmark 31 models from 10 model families and provide a holistic assessment of their strengths and weaknesses. We observe that instruction-tuning hurts knowledge recall, as pretraining-only models consistently outperform their instruction-tuned counterparts, and positive effects of model scaling, as larger models outperform smaller ones for all model families. However, the best performance from GPT-4 still represents a large gap with the upper-bound. We additionally study the role of in-context exemplars using counterfactual demonstrations, which lead to significant degradation of factual knowledge recall for large models. By further decoupling model known and unknown knowledge, we find the degradation is attributed to exemplars that contradict a model's known knowledge, as well as the number of such exemplars. Lastly, we fine-tune LLaMA-7B in different settings of known and unknown knowledge. In particular, fine-tuning on a model's known knowledge is beneficial, and consistently outperforms fine-tuning on unknown and mixed knowledge. We will make our benchmark publicly available. | [
"['Jiaqing Yuan' 'Lin Pan' 'Chung-Wei Hang' 'Jiang Guo' 'Jiarong Jiang'\n 'Bonan Min' 'Patrick Ng' 'Zhiguo Wang']"
]
|
null | null | 2404.16168 | null | null | http://arxiv.org/pdf/2404.16168v2 | 2024-05-27T23:58:00Z | 2024-04-24T19:55:50Z | The Over-Certainty Phenomenon in Modern UDA Algorithms | When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their inputs, they typically fail to account for their level of familiarity with these novel observations. This challenge becomes even more pronounced in resource-constrained settings, such as embedded systems or edge devices. To address such challenges, we aim to recalibrate a neural network's decision boundaries in relation to its cognizance of the data it observes, introducing an approach we coin as certainty distillation. While prevailing works navigate unsupervised domain adaptation (UDA) with the goal of curtailing model entropy, they unintentionally birth models that grapple with calibration inaccuracies - a dilemma we term the over-certainty phenomenon. In this paper, we probe the drawbacks of this traditional learning model. As a solution to the issue, we propose a UDA algorithm that not only augments accuracy but also assures model calibration, all while maintaining suitability for environments with limited computational resources. | [
"['Fin Amin' 'Jung-Eun Kim']"
]
|
null | null | 2404.16174 | null | null | http://arxiv.org/abs/2404.16174v1 | 2024-04-24T20:04:55Z | 2024-04-24T20:04:55Z | MiMICRI: Towards Domain-centered Counterfactual Explanations of
Cardiovascular Image Classification Models | The recent prevalence of publicly accessible, large medical imaging datasets has led to a proliferation of artificial intelligence (AI) models for cardiovascular image classification and analysis. At the same time, the potentially significant impacts of these models have motivated the development of a range of explainable AI (XAI) methods that aim to explain model predictions given certain image inputs. However, many of these methods are not developed or evaluated with domain experts, and explanations are not contextualized in terms of medical expertise or domain knowledge. In this paper, we propose a novel framework and python library, MiMICRI, that provides domain-centered counterfactual explanations of cardiovascular image classification models. MiMICRI helps users interactively select and replace segments of medical images that correspond to morphological structures. From the counterfactuals generated, users can then assess the influence of each segment on model predictions, and validate the model against known medical facts. We evaluate this library with two medical experts. Our evaluation demonstrates that a domain-centered XAI approach can enhance the interpretability of model explanations, and help experts reason about models in terms of relevant domain knowledge. However, concerns were also surfaced about the clinical plausibility of the counterfactuals generated. We conclude with a discussion on the generalizability and trustworthiness of the MiMICRI framework, as well as the implications of our findings on the development of domain-centered XAI methods for model interpretability in healthcare contexts. | [
"['Grace Guo' 'Lifu Deng' 'Animesh Tandon' 'Alex Endert' 'Bum Chul Kwon']"
]
|
null | null | 2404.16177 | null | null | http://arxiv.org/abs/2404.16177v1 | 2024-04-24T20:05:39Z | 2024-04-24T20:05:39Z | Advancing Recommender Systems by mitigating Shilling attacks | Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations. | [
"['Aditya Chichani' 'Juzer Golwala' 'Tejas Gundecha' 'Kiran Gawande']"
]
|
null | null | 2404.16179 | null | null | http://arxiv.org/pdf/2404.16179v1 | 2024-04-24T20:09:21Z | 2024-04-24T20:09:21Z | S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting
Fusion for Maximizing Anomaly Prediction in Timeseries | Anomaly detection plays a crucial role in industrial settings, particularly in maintaining the reliability and optimal performance of cooling systems. Traditional anomaly detection methods often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness. And yet traditional anomaly detection often relies on application of single models. This work proposes a novel, robust approach using five heterogeneous independent models combined with a dual ensemble fusion of voting techniques. Diverse models capture various system behaviors, while the fusion strategy maximizes detection effectiveness and minimizes false alarms. Each base autoencoder model learns a unique representation of the data, leveraging their complementary strengths to improve anomaly detection performance. To increase the effectiveness and reliability of final anomaly prediction, dual ensemble technique is applied. This approach outperforms in maximizing the coverage of identifying anomalies. Experimental results on a real-world dataset of industrial cooling system data demonstrate the effectiveness of the proposed approach. This approach can be extended to other industrial applications where anomaly detection is critical for ensuring system reliability and preventing potential malfunctions. | [
"['Sarala Naidu' 'Ning Xiong']"
]
|
null | null | 2404.16180 | null | null | http://arxiv.org/pdf/2404.16180v1 | 2024-04-24T20:10:10Z | 2024-04-24T20:10:10Z | Blind Federated Learning without initial model | Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two innovative methodologies for Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in a privacy-preserving way. In addition, one relevant contribution this research includes is the lack of an initial model in the federated learning process, making it effectively blind. This proposal is tested with several open datasets, improving both accuracy and precision. | [
"['Jose L. Salmeron' 'Irina Arévalo']"
]
|
null | null | 2404.16183 | null | null | http://arxiv.org/pdf/2404.16183v1 | 2024-04-24T20:15:57Z | 2024-04-24T20:15:57Z | ABCD: Trust enhanced Attention based Convolutional Autoencoder for Risk
Assessment | Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel Attention-based convolutional autoencoder (ABCD) for risk detection and map the risk value derive to the maintenance planning. ABCD learns the normal behavior of conductivity from historical data of a real-world industrial cooling system and reconstructs the input data, identifying anomalies that deviate from the expected patterns. The framework also employs calibration techniques to ensure the reliability of its predictions. Evaluation results demonstrate that with the attention mechanism in ABCD a 57.4% increase in performance and a reduction of false alarms by 9.37% is seen compared to without attention. The approach can effectively detect risks, the risk priority rank mapped to maintenance, providing valuable insights for cooling system designers and service personnel. Calibration error of 0.03% indicates that the model is well-calibrated and enhances model's trustworthiness, enabling informed decisions about maintenance strategies | [
"['Sarala Naidu' 'Ning Xiong']"
]
|
null | null | 2404.16188 | null | null | http://arxiv.org/pdf/2404.16188v1 | 2024-04-24T20:22:48Z | 2024-04-24T20:22:48Z | Pearls from Pebbles: Improved Confidence Functions for Auto-labeling | Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual labeling. A prominent variant, threshold-based auto-labeling (TBAL), works by finding a threshold on a model's confidence scores above which it can accurately label unlabeled data points. However, many models are known to produce overconfident scores, leading to poor TBAL performance. While a natural idea is to apply off-the-shelf calibration methods to alleviate the overconfidence issue, such methods still fall short. Rather than experimenting with ad-hoc choices of confidence functions, we propose a framework for studying the emph{optimal} TBAL confidence function. We develop a tractable version of the framework to obtain texttt{Colander} (Confidence functions for Efficient and Reliable Auto-labeling), a new post-hoc method specifically designed to maximize performance in TBAL systems. We perform an extensive empirical evaluation of our method texttt{Colander} and compare it against methods designed for calibration. texttt{Colander} achieves up to 60% improvements on coverage over the baselines while maintaining auto-labeling error below $5%$ and using the same amount of labeled data as the baselines. | [
"['Harit Vishwakarma' 'Reid' 'Chen' 'Sui Jiet Tay'\n 'Satya Sai Srinath Namburi' 'Frederic Sala' 'Ramya Korlakai Vinayak']"
]
|
null | null | 2404.16193 | null | null | http://arxiv.org/pdf/2404.16193v1 | 2024-04-24T20:33:25Z | 2024-04-24T20:33:25Z | Improving Multi-label Recognition using Class Co-Occurrence
Probabilities | Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged information from vision-language models (VLMs) trained on large text-images datasets for the task. These methods learn an independent classifier for each object (class), overlooking correlations in their occurrences. Such co-occurrences can be captured from the training data as conditional probabilities between a pair of classes. We propose a framework to extend the independent classifiers by incorporating the co-occurrence information for object pairs to improve the performance of independent classifiers. We use a Graph Convolutional Network (GCN) to enforce the conditional probabilities between classes, by refining the initial estimates derived from image and text sources obtained using VLMs. We validate our method on four MLR datasets, where our approach outperforms all state-of-the-art methods. | [
"['Samyak Rawlekar' 'Shubhang Bhatnagar'\n 'Vishnuvardhan Pogunulu Srinivasulu' 'Narendra Ahuja']"
]
|
null | null | 2404.16196 | null | null | http://arxiv.org/pdf/2404.16196v1 | 2024-04-24T20:35:17Z | 2024-04-24T20:35:17Z | ApisTox: a new benchmark dataset for the classification of small
molecules toxicity on honey bees | The global decline in bee populations poses significant risks to agriculture, biodiversity, and environmental stability. To bridge the gap in existing data, we introduce ApisTox, a comprehensive dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. This makes it a valuable tool for both academic research and practical applications in bee conservation. | [
"['Jakub Adamczyk' 'Jakub Poziemski' 'Paweł Siedlecki']"
]
|
null | null | 2404.16212 | null | null | http://arxiv.org/pdf/2404.16212v1 | 2024-04-24T21:21:50Z | 2024-04-24T21:21:50Z | An Analysis of Recent Advances in Deepfake Image Detection in an
Evolving Threat Landscape | Deepfake or synthetic images produced using deep generative models pose serious risks to online platforms. This has triggered several research efforts to accurately detect deepfake images, achieving excellent performance on publicly available deepfake datasets. In this work, we study 8 state-of-the-art detectors and argue that they are far from being ready for deployment due to two recent developments. First, the emergence of lightweight methods to customize large generative models, can enable an attacker to create many customized generators (to create deepfakes), thereby substantially increasing the threat surface. We show that existing defenses fail to generalize well to such emph{user-customized generative models} that are publicly available today. We discuss new machine learning approaches based on content-agnostic features, and ensemble modeling to improve generalization performance against user-customized models. Second, the emergence of textit{vision foundation models} -- machine learning models trained on broad data that can be easily adapted to several downstream tasks -- can be misused by attackers to craft adversarial deepfakes that can evade existing defenses. We propose a simple adversarial attack that leverages existing foundation models to craft adversarial samples textit{without adding any adversarial noise}, through careful semantic manipulation of the image content. We highlight the vulnerabilities of several defenses against our attack, and explore directions leveraging advanced foundation models and adversarial training to defend against this new threat. | [
"['Sifat Muhammad Abdullah' 'Aravind Cheruvu' 'Shravya Kanchi'\n 'Taejoong Chung' 'Peng Gao' 'Murtuza Jadliwala' 'Bimal Viswanath']"
]
|
null | null | 2404.16218 | null | null | http://arxiv.org/abs/2404.16218v1 | 2024-04-24T21:33:17Z | 2024-04-24T21:33:17Z | Efficient NAS with FaDE on Hierarchical Spaces | Neural architecture search (NAS) is a challenging problem. Hierarchical search spaces allow for cheap evaluations of neural network sub modules to serve as surrogate for architecture evaluations. Yet, sometimes the hierarchy is too restrictive or the surrogate fails to generalize. We present FaDE which uses differentiable architecture search to obtain relative performance predictions on finite regions of a hierarchical NAS space. The relative nature of these ranks calls for a memory-less, batch-wise outer search algorithm for which we use an evolutionary algorithm with pseudo-gradient descent. FaDE is especially suited on deep hierarchical, respectively multi-cell search spaces, which it can explore by linear instead of exponential cost and therefore eliminates the need for a proxy search space. Our experiments show that firstly, FaDE-ranks on finite regions of the search space correlate with corresponding architecture performances and secondly, the ranks can empower a pseudo-gradient evolutionary search on the complete neural architecture search space. | [
"['Simon Neumeyer' 'Julian Stier' 'Michael Granitzer']"
]
|
null | null | 2404.16233 | null | null | http://arxiv.org/pdf/2404.16233v2 | 2024-04-30T21:09:27Z | 2024-04-24T22:28:12Z | AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with
Foundation Models | AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities spanning classification, regression, object detection, semantic matching, and image segmentation. Experiments across diverse datasets and tasks showcases AutoMM's superior performance in basic classification and regression tasks compared to existing AutoML tools, while also demonstrating competitive results in advanced tasks, aligning with specialized toolboxes designed for such purposes. | [
"['Zhiqiang Tang' 'Haoyang Fang' 'Su Zhou' 'Taojiannan Yang' 'Zihan Zhong'\n 'Tony Hu' 'Katrin Kirchhoff' 'George Karypis']"
]
|
null | null | 2404.16260 | null | null | http://arxiv.org/abs/2404.16260v1 | 2024-04-25T00:10:25Z | 2024-04-25T00:10:25Z | OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search | In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8%$ relevance, $>7%$ engagement, and $>5%$ ads CTR in Pinterest's production search system. The main contributors to these gains are improved content understanding, better multi-task learning, and real-time serving. We enrich our entity representations using diverse text derived from image captions from a generative LLM, historical engagement, and user-curated boards. Our multitask learning setup produces a single search query embedding in the same space as pin and product embeddings and compatible with pre-existing pin and product embeddings. We show the value of each feature through ablation studies, and show the effectiveness of a unified model compared to standalone counterparts. Finally, we share how these embeddings have been deployed across the Pinterest search stack, from retrieval to ranking, scaling to serve $300k$ requests per second at low latency. Our implementation of this work is available at https://github.com/pinterest/atg-research/tree/main/omnisearchsage. | [
"['Prabhat Agarwal' 'Minhazul Islam Sk' 'Nikil Pancha'\n 'Kurchi Subhra Hazra' 'Jiajing Xu' 'Chuck Rosenberg']"
]
|
null | null | 2404.16277 | null | null | http://arxiv.org/pdf/2404.16277v1 | 2024-04-25T01:33:55Z | 2024-04-25T01:33:55Z | Causally Inspired Regularization Enables Domain General Representations | Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can identify domain-general (non-spurious) feature representations. For the standard input-output predictive setting, we categorize the set of graphs considered in the literature into two distinct groups: (i) those in which the empirical risk minimizer across training domains gives domain-general representations and (ii) those where it does not. For the latter case (ii), we propose a novel framework with regularizations, which we demonstrate are sufficient for identifying domain-general feature representations without a priori knowledge (or proxies) of the spurious features. Empirically, our proposed method is effective for both (semi) synthetic and real-world data, outperforming other state-of-the-art methods in average and worst-domain transfer accuracy. | [
"['Olawale Salaudeen' 'Sanmi Koyejo']"
]
|
null | null | 2404.16280 | null | null | http://arxiv.org/pdf/2404.16280v1 | 2024-04-25T01:48:44Z | 2024-04-25T01:48:44Z | An Efficient Reconstructed Differential Evolution Variant by Some of the
Current State-of-the-art Strategies for Solving Single Objective Bound
Constrained Problems | Complex single-objective bounded problems are often difficult to solve. In evolutionary computation methods, since the proposal of differential evolution algorithm in 1997, it has been widely studied and developed due to its simplicity and efficiency. These developments include various adaptive strategies, operator improvements, and the introduction of other search methods. After 2014, research based on LSHADE has also been widely studied by researchers. However, although recently proposed improvement strategies have shown superiority over their previous generation's first performance, adding all new strategies may not necessarily bring the strongest performance. Therefore, we recombine some effective advances based on advanced differential evolution variants in recent years and finally determine an effective combination scheme to further promote the performance of differential evolution. In this paper, we propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems. Based on the benchmark suite of the 2024 IEEE Congress on Evolutionary Computation (CEC2024), we tested RDE and several other advanced differential evolution variants. The experimental results show that RDE has superior performance in solving complex optimization problems. | [
"['Sichen Tao' 'Ruihan Zhao' 'Kaiyu Wang' 'Shangce Gao']"
]
|
null | null | 2404.16281 | null | null | http://arxiv.org/abs/2404.16281v2 | 2024-06-19T19:09:20Z | 2024-04-25T01:53:21Z | Timely Communications for Remote Inference | In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network blue infers a time-varying target (e.g., the locations of vehicles and pedestrians) based on features (e.g., video frames) observed at a sensing node (e.g., a camera). One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain, whereas it is not true if the data sequence is far from being Markovian. Hence, the inference error is a function of Age of Information (AoI), where the function could be non-monotonic. To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. In addition, we design low-complexity scheduling policies to improve inference performance. For single-source, single-channel systems, we provide an optimal scheduling policy. In multi-source, multi-channel systems, the scheduling problem becomes a multi-action restless multi-armed bandit problem. For this setting, we design a new scheduling policy by integrating Whittle index-based source selection and duality-based feature selection-from-buffer algorithms. This new scheduling policy is proven to be asymptotically optimal. These scheduling results hold for minimizing general AoI functions (monotonic or non-monotonic). Data-driven evaluations demonstrate the significant advantages of our proposed scheduling policies. | [
"['Md Kamran Chowdhury Shisher' 'Yin Sun' 'I-Hong Hou']"
]
|
null | null | 2404.16283 | null | null | http://arxiv.org/pdf/2404.16283v1 | 2024-04-25T01:56:00Z | 2024-04-25T01:56:00Z | Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text
Streaming Services | The advent of large language models (LLMs) has transformed text-based services, enabling capabilities ranging from real-time translation to AI-driven chatbots. However, existing serving systems primarily focus on optimizing server-side aggregate metrics like token generation throughput, ignoring individual user experience with streamed text. As a result, under high and/or bursty load, a significant number of users can receive unfavorable service quality or poor Quality-of-Experience (QoE). In this paper, we first formally define QoE of text streaming services, where text is delivered incrementally and interactively to users, by considering the end-to-end token delivery process throughout the entire interaction with the user. Thereafter, we propose Andes, a QoE-aware serving system that enhances user experience for LLM-enabled text streaming services. At its core, Andes strategically allocates contended GPU resources among multiple requests over time to optimize their QoE. Our evaluations demonstrate that, compared to the state-of-the-art LLM serving systems like vLLM, Andes improves the average QoE by up to 3.2$times$ under high request rate, or alternatively, it attains up to 1.6$times$ higher request rate while preserving high QoE. | [
"['Jiachen Liu' 'Zhiyu Wu' 'Jae-Won Chung' 'Fan Lai' 'Myungjin Lee'\n 'Mosharaf Chowdhury']"
]
|
null | null | 2404.16287 | null | null | http://arxiv.org/pdf/2404.16287v1 | 2024-04-25T02:14:07Z | 2024-04-25T02:14:07Z | Differentially Private Federated Learning: Servers Trustworthiness,
Estimation, and Statistical Inference | Differentially private federated learning is crucial for maintaining privacy in distributed environments. This paper investigates the challenges of high-dimensional estimation and inference under the constraints of differential privacy. First, we study scenarios involving an untrusted central server, demonstrating the inherent difficulties of accurate estimation in high-dimensional problems. Our findings indicate that the tight minimax rates depends on the high-dimensionality of the data even with sparsity assumptions. Second, we consider a scenario with a trusted central server and introduce a novel federated estimation algorithm tailored for linear regression models. This algorithm effectively handles the slight variations among models distributed across different machines. We also propose methods for statistical inference, including coordinate-wise confidence intervals for individual parameters and strategies for simultaneous inference. Extensive simulation experiments support our theoretical advances, underscoring the efficacy and reliability of our approaches. | [
"['Zhe Zhang' 'Ryumei Nakada' 'Linjun Zhang']"
]
|
null | null | 2404.16292 | null | null | http://arxiv.org/abs/2404.16292v1 | 2024-04-25T02:23:11Z | 2024-04-25T02:23:11Z | One Noise to Rule Them All: Learning a Unified Model of
Spatially-Varying Noise Patterns | Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters and a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance. | [
"['Arman Maesumi' 'Dylan Hu' 'Krishi Saripalli' 'Vladimir G. Kim'\n 'Matthew Fisher' 'Sören Pirk' 'Daniel Ritchie']"
]
|
null | null | 2404.16300 | null | null | http://arxiv.org/pdf/2404.16300v1 | 2024-04-25T02:48:16Z | 2024-04-25T02:48:16Z | Reinforcement Learning with Generative Models for Compact Support Sets | Foundation models contain a wealth of information from their vast number of training samples. However, most prior arts fail to extract this information in a precise and efficient way for small sample sizes. In this work, we propose a framework utilizing reinforcement learning as a control for foundation models, allowing for the granular generation of small, focused synthetic support sets to augment the performance of neural network models on real data classification tasks. We first allow a reinforcement learning agent access to a novel context based dictionary; the agent then uses this dictionary with a novel prompt structure to form and optimize prompts as inputs to generative models, receiving feedback based on a reward function combining the change in validation accuracy and entropy. A support set is formed this way over several exploration steps. Our framework produced excellent results, increasing classification accuracy by significant margins for no additional labelling or data cost. | [
"['Nico Schiavone' 'Xingyu Li']"
]
|
null | null | 2404.16307 | null | null | http://arxiv.org/pdf/2404.16307v2 | 2024-06-01T06:20:54Z | 2024-04-25T03:22:48Z | Boosting Model Resilience via Implicit Adversarial Data Augmentation | Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial perturbation distributions, enabling adaptive adjustment in the learning difficulty tailored to each sample's specific characteristics. We then theoretically reveal that our augmentation process approximates the optimization of a surrogate loss function as the number of augmented copies increases indefinitely. This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process. We conduct extensive experiments across four common biased learning scenarios: long-tail learning, generalized long-tail learning, noisy label learning, and subpopulation shift learning. The empirical results demonstrate that our method consistently achieves state-of-the-art performance, highlighting its broad adaptability. | [
"['Xiaoling Zhou' 'Wei Ye' 'Zhemg Lee' 'Rui Xie' 'Shikun Zhang']"
]
|
null | null | 2404.16317 | null | null | http://arxiv.org/pdf/2404.16317v1 | 2024-04-25T03:46:53Z | 2024-04-25T03:46:53Z | FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor
Contraction | Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging. This paper introduces FLAASH, a flexible and modular accelerator design for sparse tensor contraction that achieves over 25x speedup for a deep learning workload. Our architecture performs sparse high-order tensor contraction by distributing sparse dot products, or portions thereof, to numerous Sparse Dot Product Engines (SDPEs). Memory structure and job distribution can be customized, and we demonstrate a simple approach as a proof of concept. We address the challenges associated with control flow to navigate data structures, high-order representation, and high-sparsity handling. The effectiveness of our approach is demonstrated through various evaluations, showcasing significant speedup as sparsity and order increase. | [
"['Gabriel Kulp' 'Andrew Ensinger' 'Lizhong Chen']"
]
|
null | null | 2404.16324 | null | null | http://arxiv.org/pdf/2404.16324v1 | 2024-04-25T04:21:56Z | 2024-04-25T04:21:56Z | Improved impedance inversion by deep learning and iterated graph
Laplacian | Deep learning techniques have shown significant potential in many applications through recent years. The achieved results often outperform traditional techniques. However, the quality of a neural network highly depends on the used training data. Noisy, insufficient, or biased training data leads to suboptimal results. We present a hybrid method that combines deep learning with iterated graph Laplacian and show its application in acoustic impedance inversion which is a routine procedure in seismic explorations. A neural network is used to obtain a first approximation of the underlying acoustic impedance and construct a graph Laplacian matrix from this approximation. Afterwards, we use a Tikhonov-like variational method to solve the impedance inversion problem where the regularizer is based on the constructed graph Laplacian. The obtained solution can be shown to be more accurate and stable with respect to noise than the initial guess obtained by the neural network. This process can be iterated several times, each time constructing a new graph Laplacian matrix from the most recent reconstruction. The method converges after only a few iterations returning a much more accurate reconstruction. We demonstrate the potential of our method on two different datasets and under various levels of noise. We use two different neural networks that have been introduced in previous works. The experiments show that our approach improves the reconstruction quality in the presence of noise. | [
"['Davide Bianchi' 'Florian Bossmann' 'Wenlong Wang' 'Mingming Liu']"
]
|
null | null | 2404.16326 | null | null | http://arxiv.org/pdf/2404.16326v1 | 2024-04-25T04:23:40Z | 2024-04-25T04:23:40Z | NeuroKoopman Dynamic Causal Discovery | In many real-world applications where the system dynamics has an underlying interdependency among its variables (such as power grid, economics, neuroscience, omics networks, environmental ecosystems, and others), one is often interested in knowing whether the past values of one time series influences the future of another, known as Granger causality, and the associated underlying dynamics. This paper introduces a Koopman-inspired framework that leverages neural networks for data-driven learning of the Koopman bases, termed NeuroKoopman Dynamic Causal Discovery (NKDCD), for reliably inferring the Granger causality along with the underlying nonlinear dynamics. NKDCD employs an autoencoder architecture that lifts the nonlinear dynamics to a higher dimension using data-learned bases, where the lifted time series can be reliably modeled linearly. The lifting function, the linear Granger causality lag matrices, and the projection function (from lifted space to base space) are all represented as multilayer perceptrons and are all learned simultaneously in one go. NKDCD also utilizes sparsity-inducing penalties on the weights of the lag matrices, encouraging the model to select only the needed causal dependencies within the data. Through extensive testing on practically applicable datasets, it is shown that the NKDCD outperforms the existing nonlinear Granger causality discovery approaches. | [
"['Rahmat Adesunkanmi' 'Balaji Sesha Srikanth Pokuri' 'Ratnesh Kumar']"
]
|
null | null | 2404.16328 | null | null | http://arxiv.org/pdf/2404.16328v1 | 2024-04-25T04:29:25Z | 2024-04-25T04:29:25Z | Distributionally Robust Safe Screening | In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are subject to uncertainty within a predetermined range. By extending the SS technique to accommodate this weight uncertainty, the DRSS method is capable of reliably identifying unnecessary samples and features under any future distribution within a specified range. We provide a theoretical guarantee of the DRSS method and validate its performance through numerical experiments on both synthetic and real-world datasets. | [
"['Hiroyuki Hanada' 'Satoshi Akahane' 'Tatsuya Aoyama' 'Tomonari Tanaka'\n 'Yoshito Okura' 'Yu Inatsu' 'Noriaki Hashimoto' 'Taro Murayama'\n 'Lee Hanju' 'Shinya Kojima' 'Ichiro Takeuchi']"
]
|
null | null | 2404.16336 | null | null | http://arxiv.org/pdf/2404.16336v1 | 2024-04-25T04:53:43Z | 2024-04-25T04:53:43Z | FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art
Commissions | The unique artistic style is crucial to artists' occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists' concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle. | [
"['Changjuan Ran' 'Yeting Guo' 'Fang Liu' 'Shenglan Cui' 'Yunfan Ye']"
]
|
null | null | 2404.16356 | null | null | http://arxiv.org/pdf/2404.16356v1 | 2024-04-25T06:22:21Z | 2024-04-25T06:22:21Z | Integration of Mixture of Experts and Multimodal Generative AI in
Internet of Vehicles: A Survey | Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. | [
"['Minrui Xu' 'Dusit Niyato' 'Jiawen Kang' 'Zehui Xiong' 'Abbas Jamalipour'\n 'Yuguang Fang' 'Dong In Kim' 'Xuemin' 'Shen']"
]
|
null | null | 2404.16361 | null | null | http://arxiv.org/pdf/2404.16361v1 | 2024-04-25T06:42:32Z | 2024-04-25T06:42:32Z | Evolutionary Causal Discovery with Relative Impact Stratification for
Interpretable Data Analysis | This study proposes Evolutionary Causal Discovery (ECD) for causal discovery that tailors response variables, predictor variables, and corresponding operators to research datasets. Utilizing genetic programming for variable relationship parsing, the method proceeds with the Relative Impact Stratification (RIS) algorithm to assess the relative impact of predictor variables on the response variable, facilitating expression simplification and enhancing the interpretability of variable relationships. ECD proposes an expression tree to visualize the RIS results, offering a differentiated depiction of unknown causal relationships compared to conventional causal discovery. The ECD method represents an evolution and augmentation of existing causal discovery methods, providing an interpretable approach for analyzing variable relationships in complex systems, particularly in healthcare settings with Electronic Health Record (EHR) data. Experiments on both synthetic and real-world EHR datasets demonstrate the efficacy of ECD in uncovering patterns and mechanisms among variables, maintaining high accuracy and stability across different noise levels. On the real-world EHR dataset, ECD reveals the intricate relationships between the response variable and other predictive variables, aligning with the results of structural equation modeling and shapley additive explanations analyses. | [
"['Ou Deng' 'Shoji Nishimura' 'Atsushi Ogihara' 'Qun Jin']"
]
|
null | null | 2404.16366 | null | null | http://arxiv.org/pdf/2404.16366v1 | 2024-04-25T07:09:05Z | 2024-04-25T07:09:05Z | Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection | Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods. This is motivated by the hypothesis that nodes in the graph tend to exhibit consistent behaviors with their neighborhoods. However, such consistency can be disrupted by graph anomalies in multiple ways. Most existing methods directly employ GNNs to learn representations, disregarding the negative impact of graph anomalies on GNNs, resulting in sub-optimal node representations and anomaly detection performance. While a few recent approaches have redesigned GNNs for graph anomaly detection under semi-supervised label guidance, how to address the adverse effects of graph anomalies on GNNs in unsupervised scenarios and learn effective representations for anomaly detection are still under-explored. To bridge this gap, in this paper, we propose a simple yet effective framework for Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD). Specifically, G3AD introduces two auxiliary networks along with correlation constraints to guard the GNNs from inconsistent information encoding. Furthermore, G3AD introduces an adaptive caching module to guard the GNNs from solely reconstructing the observed data that contains anomalies. Extensive experiments demonstrate that our proposed G3AD can outperform seventeen state-of-the-art methods on both synthetic and real-world datasets. | [
"['Yuanchen Bei' 'Sheng Zhou' 'Jinke Shi' 'Yao Ma' 'Haishuai Wang'\n 'Jiajun Bu']"
]
|
null | null | 2404.16367 | null | null | http://arxiv.org/pdf/2404.16367v2 | 2024-05-31T23:47:15Z | 2024-04-25T07:10:29Z | Learning Syntax Without Planting Trees: Understanding When and Why
Transformers Generalize Hierarchically | Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could cause such generalization behavior to emerge. We extensively experiment with transformer models trained on multiple synthetic datasets and with different training objectives and show that while other objectives e.g. sequence-to-sequence modeling, prefix language modeling, often failed to lead to hierarchical generalization, models trained with the language modeling objective consistently learned to generalize hierarchically. We then conduct pruning experiments to study how transformers trained with the language modeling objective encode hierarchical structure. When pruned, we find joint existence of subnetworks within the model with different generalization behaviors (subnetworks corresponding to hierarchical structure and linear order). Finally, we take a Bayesian perspective to further uncover transformers' preference for hierarchical generalization: We establish a correlation between whether transformers generalize hierarchically on a dataset and whether the simplest explanation of that dataset is provided by a hierarchical grammar compared to regular grammars exhibiting linear generalization. | [
"['Kabir Ahuja' 'Vidhisha Balachandran' 'Madhur Panwar' 'Tianxing He'\n 'Noah A. Smith' 'Navin Goyal' 'Yulia Tsvetkov']"
]
|
null | null | 2404.16397 | null | null | http://arxiv.org/pdf/2404.16397v1 | 2024-04-25T08:15:37Z | 2024-04-25T08:15:37Z | Deep Learning-based Prediction of Breast Cancer Tumor and Immune
Phenotypes from Histopathology | The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology. | [
"['Tiago Gonçalves' 'Dagoberto Pulido-Arias' 'Julian Willett'\n 'Katharina V. Hoebel' 'Mason Cleveland' 'Syed Rakin Ahmed'\n 'Elizabeth Gerstner' 'Jayashree Kalpathy-Cramer' 'Jaime S. Cardoso'\n 'Christopher P. Bridge' 'Albert E. Kim']"
]
|
null | null | 2404.16399 | null | null | http://arxiv.org/pdf/2404.16399v1 | 2024-04-25T08:22:47Z | 2024-04-25T08:22:47Z | Offline Reinforcement Learning with Behavioral Supervisor Tuning | Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key caveat: they demand substantial per-dataset hyperparameter tuning to achieve reported performance, which requires policy rollouts in the environment to evaluate; this can rapidly become cumbersome. Furthermore, substantial tuning requirements can hamper the adoption of these algorithms in practical domains. In this paper, we present TD3 with Behavioral Supervisor Tuning (TD3-BST), an algorithm that trains an uncertainty model and uses it to guide the policy to select actions within the dataset support. TD3-BST can learn more effective policies from offline datasets compared to previous methods and achieves the best performance across challenging benchmarks without requiring per-dataset tuning. | [
"['Padmanaba Srinivasan' 'William Knottenbelt']"
]
|
null | null | 2404.16417 | null | null | http://arxiv.org/pdf/2404.16417v1 | 2024-04-25T08:49:29Z | 2024-04-25T08:49:29Z | Constructing Optimal Noise Channels for Enhanced Robustness in Quantum
Machine Learning | With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident. In this work, we outline the connection between quantum noise channels and differential privacy (DP), by constructing a family of noise channels which are inherently $epsilon$-DP: $(alpha, gamma)$-channels. Through this approach, we successfully replicate the $epsilon$-DP bounds observed for depolarizing and random rotation channels, thereby affirming the broad generality of our framework. Additionally, we use a semi-definite program to construct an optimally robust channel. In a small-scale experimental evaluation, we demonstrate the benefits of using our optimal noise channel over depolarizing noise, particularly in enhancing adversarial accuracy. Moreover, we assess how the variables $alpha$ and $gamma$ affect the certifiable robustness and investigate how different encoding methods impact the classifier's robustness. | [
"['David Winderl' 'Nicola Franco' 'Jeanette Miriam Lorenz']"
]
|
null | null | 2404.16436 | null | null | http://arxiv.org/pdf/2404.16436v2 | 2024-05-07T12:42:32Z | 2024-04-25T09:12:35Z | Leveraging tropical reef, bird and unrelated sounds for superior
transfer learning in marine bioacoustics | Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef sounds, though modest compared to bird libraries at 2% of the sample count. Through testing few-shot transfer learning performance, we observe that pretraining on bird audio provides notably superior generalizability compared to pretraining on ReefSet or unrelated audio alone. However, our key findings show that cross-domain mixing which leverages bird, reef and unrelated audio during pretraining maximizes reef generalizability. SurfPerch, our pretrained network, provides a strong foundation for automated analysis of marine PAM data with minimal annotation and compute costs. | [
"['Ben Williams' 'Bart van Merriënboer' 'Vincent Dumoulin' 'Jenny Hamer'\n 'Eleni Triantafillou' 'Abram B. Fleishman' 'Matthew McKown'\n 'Jill E. Munger' 'Aaron N. Rice' 'Ashlee Lillis' 'Clemency E. White'\n 'Catherine A. D. Hobbs' 'Tries B. Razak' 'Kate E. Jones' 'Tom Denton']"
]
|
null | null | 2404.16444 | null | null | http://arxiv.org/pdf/2404.16444v2 | 2024-05-02T09:31:10Z | 2024-04-25T09:23:03Z | Automating the Discovery of Partial Differential Equations in Dynamical
Systems | Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task. We present an extension to the ARGOS framework, ARGOS-RAL, which leverages sparse regression with the recurrent adaptive lasso to identify PDEs from limited prior knowledge automatically. Our method automates calculating partial derivatives, constructing a candidate library, and estimating a sparse model. We rigorously evaluate the performance of ARGOS-RAL in identifying canonical PDEs under various noise levels and sample sizes, demonstrating its robustness in handling noisy and non-uniformly distributed data. We also test the algorithm's performance on datasets consisting solely of random noise to simulate scenarios with severely compromised data quality. Our results show that ARGOS-RAL effectively and reliably identifies the underlying PDEs from data, outperforming the sequential threshold ridge regression method in most cases. We highlight the potential of combining statistical methods, machine learning, and dynamical systems theory to automatically discover governing equations from collected data, streamlining the scientific modeling process. | [
"['Weizhen Li' 'Rui Carvalho']"
]
|
null | null | 2404.16468 | null | null | http://arxiv.org/pdf/2404.16468v1 | 2024-04-25T09:50:57Z | 2024-04-25T09:50:57Z | A Dual Perspective of Reinforcement Learning for Imposing Policy
Constraints | Model-free reinforcement learning methods lack an inherent mechanism to impose behavioural constraints on the trained policies. While certain extensions exist, they remain limited to specific types of constraints, such as value constraints with additional reward signals or visitation density constraints. In this work we try to unify these existing techniques and bridge the gap with classical optimization and control theory, using a generic primal-dual framework for value-based and actor-critic reinforcement learning methods. The obtained dual formulations turn out to be especially useful for imposing additional constraints on the learned policy, as an intrinsic relationship between such dual constraints (or regularization terms) and reward modifications in the primal is reveiled. Furthermore, using this framework, we are able to introduce some novel types of constraints, allowing to impose bounds on the policy's action density or on costs associated with transitions between consecutive states and actions. From the adjusted primal-dual optimization problems, a practical algorithm is derived that supports various combinations of policy constraints that are automatically handled throughout training using trainable reward modifications. The resulting $texttt{DualCRL}$ method is examined in more detail and evaluated under different (combinations of) constraints on two interpretable environments. The results highlight the efficacy of the method, which ultimately provides the designer of such systems with a versatile toolbox of possible policy constraints. | [
"['Bram De Cooman' 'Johan Suykens']"
]
|
null | null | 2404.16495 | null | null | http://arxiv.org/pdf/2404.16495v1 | 2024-04-25T10:40:49Z | 2024-04-25T10:40:49Z | T-Explainer: A Model-Agnostic Explainability Framework Based on
Gradients | The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and generalize intricate patterns hidden in massive datasets. Modern learning models, while powerful, often exhibit a level of complexity that renders them opaque black boxes, resulting in a notable lack of transparency that hinders our ability to decipher their decision-making processes. Opacity challenges the interpretability and practical application of machine learning, especially in critical domains where understanding the underlying reasons is essential for informed decision-making. Explainable Artificial Intelligence (XAI) rises to meet that challenge, unraveling the complexity of black boxes by providing elucidating explanations. Among the various XAI approaches, feature attribution/importance XAI stands out for its capacity to delineate the significance of input features in the prediction process. However, most existing attribution methods have limitations, such as instability, when divergent explanations may result from similar or even the same instance. In this work, we introduce T-Explainer, a novel local additive attribution explainer based on Taylor expansion endowed with desirable properties, such as local accuracy and consistency, while stable over multiple runs. We demonstrate T-Explainer's effectiveness through benchmark experiments with well-known attribution methods. In addition, T-Explainer is developed as a comprehensive XAI framework comprising quantitative metrics to assess and visualize attribution explanations. | [
"['Evandro S. Ortigossa' 'Fábio F. Dias' 'Brian Barr' 'Claudio T. Silva'\n 'Luis Gustavo Nonato']"
]
|
null | null | 2404.16496 | null | null | http://arxiv.org/pdf/2404.16496v1 | 2024-04-25T10:41:12Z | 2024-04-25T10:41:12Z | Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring | We provide a condition monitoring system for wind farms, based on normal behaviour modelling using a probabilistic multi-layer perceptron with transfer learning via fine-tuning. The model predicts the output power of the wind turbine under normal behaviour based on features retrieved from supervisory control and data acquisition (SCADA) systems. Its advantages are that (i) it can be trained with SCADA data of at least a few years, (ii) it can incorporate all SCADA data of all wind turbines in a wind farm as features, (iii) it assumes that the output power follows a normal density with heteroscedastic variance and (iv) it can predict the output of one wind turbine by borrowing strength from the data of all other wind turbines in a farm. Probabilistic guidelines for condition monitoring are given via a CUSUM control chart. We illustrate the performance of our model in a real SCADA data example which provides evidence that it outperforms other probabilistic prediction models. | [
"['Filippo Fiocchi' 'Domna Ladopoulou' 'Petros Dellaportas']"
]
|
null | null | 2404.16505 | null | null | http://arxiv.org/pdf/2404.16505v1 | 2024-04-25T10:57:50Z | 2024-04-25T10:57:50Z | Efficient algorithms for regularized Poisson Non-negative Matrix
Factorization | We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth functions, alongside linear constraints. This problem holds significant relevance in numerous Machine Learning applications, particularly within the domain of physical linear unmixing problems. A notable challenge arises from the main loss term in the Poisson NMF problem being a KL divergence, which is non-Lipschitz, rendering traditional gradient descent-based approaches inefficient. In this contribution, we explore the utilization of Block Successive Upper Minimization (BSUM) to overcome this challenge. We build approriate majorizing function for Lipschitz and relatively smooth functions, and show how to introduce linear constraints into the problem. This results in the development of two novel algorithms for regularized Poisson NMF. We conduct numerical simulations to showcase the effectiveness of our approach. | [
"['Nathanaël Perraudin' 'Adrien Teutrie' 'Cécile Hébert'\n 'Guillaume Obozinski']"
]
|
null | null | 2404.16519 | null | null | http://arxiv.org/pdf/2404.16519v1 | 2024-04-25T11:22:48Z | 2024-04-25T11:22:48Z | Unbiased Estimating Equation on Inverse Divergence and Its Conditions | This paper focuses on the Bregman divergence defined by the reciprocal function, called the inverse divergence. For the loss function defined by the monotonically increasing function $f$ and inverse divergence, the conditions for the statistical model and function $f$ under which the estimating equation is unbiased are clarified. Specifically, we characterize two types of statistical models, an inverse Gaussian type and a mixture of generalized inverse Gaussian type distributions, to show that the conditions for the function $f$ are different for each model. We also define Bregman divergence as a linear sum over the dimensions of the inverse divergence and extend the results to the multi-dimensional case. | [
"['Masahiro Kobayashi' 'Kazuho Watanabe']"
]
|
null | null | 2404.16522 | null | null | http://arxiv.org/pdf/2404.16522v1 | 2024-04-25T11:27:58Z | 2024-04-25T11:27:58Z | A Deep Learning-Driven Pipeline for Differentiating Hypertrophic
Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography | Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis. | [
"['Bo Peng' 'Xiaofeng Li' 'Xinyu Li' 'Zhenghan Wang' 'Hui Deng'\n 'Xiaoxian Luo' 'Lixue Yin' 'Hongmei Zhang']"
]
|
null | null | 2404.16532 | null | null | http://arxiv.org/pdf/2404.16532v1 | 2024-04-25T11:43:46Z | 2024-04-25T11:43:46Z | Global Concept Explanations for Graphs by Contrastive Learning | Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior human intuition exists. To that end, we propose a method to extract global concept explanations from the predictions of graph neural networks to develop a deeper understanding of the tasks underlying structure-property relationships. We identify concept explanations as dense clusters in the self-explaining Megan models subgraph latent space. For each concept, we optimize a representative prototype graph and optionally use GPT-4 to provide hypotheses about why each structure has a certain effect on the prediction. We conduct computational experiments on synthetic and real-world graph property prediction tasks. For the synthetic tasks we find that our method correctly reproduces the structural rules by which they were created. For real-world molecular property regression and classification tasks, we find that our method rediscovers established rules of thumb. More specifically, our results for molecular mutagenicity prediction indicate more fine-grained resolution of structural details than existing explainability methods, consistent with previous results from chemistry literature. Overall, our results show promising capability to extract the underlying structure-property relationships for complex graph property prediction tasks. | [
"['Jonas Teufel' 'Pascal Friederich']"
]
|
null | null | 2404.16549 | null | null | http://arxiv.org/pdf/2404.16549v2 | 2024-05-03T06:32:29Z | 2024-04-25T12:04:36Z | Application of Long-Short Term Memory and Convolutional Neural Networks
for Real-Time Bridge Scour Prediction | Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting using data collected from bridges in Alaska and Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE) ranging from 0.1m to 0.5m for predicting bed level variations a week in advance, showing a reasonable performance. The Fully Convolutional Network (FCN) variant of CNN outperformed other CNN configurations, showing a comparable performance to LSTMs with significantly lower computational costs. We explored various innovative random-search heuristics for hyperparameter tuning and model optimisation which resulted in reduced computational cost compared to grid-search method. The impact of different combinations of sensor features on scour prediction showed the significance of the historical time series of scour for predicting upcoming events. Overall, this study provides a greater understanding of the potential of Deep Learning algorithms for real-time scour prediction and early warning for bridges with distinct geology, geomorphology and flow characteristics. | [
"['Tahrima Hashem' 'Negin Yousefpour']"
]
|
null | null | 2404.16551 | null | null | http://arxiv.org/pdf/2404.16551v1 | 2024-04-25T12:07:41Z | 2024-04-25T12:07:41Z | Surprisingly Strong Performance Prediction with Neural Graph Features | Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Although many performance predictors correlate well with ground truth performance, they require training data in the form of trained networks. Recently, zero-cost proxies have been proposed as an efficient method to estimate network performance without any training. However, they are still poorly understood, exhibit biases with network properties, and their performance is limited. Inspired by the drawbacks of zero-cost proxies, we propose neural graph features (GRAF), simple to compute properties of architectural graphs. GRAF offers fast and interpretable performance prediction while outperforming zero-cost proxies and other common encodings. In combination with other zero-cost proxies, GRAF outperforms most existing performance predictors at a fraction of the cost. | [
"['Gabriela Kadlecová' 'Jovita Lukasik' 'Martin Pilát' 'Petra Vidnerová'\n 'Mahmoud Safari' 'Roman Neruda' 'Frank Hutter']"
]
|
null | null | 2404.16553 | null | null | http://arxiv.org/abs/2404.16553v1 | 2024-04-25T12:09:17Z | 2024-04-25T12:09:17Z | RE-RecSys: An End-to-End system for recommending properties in
Real-Estate domain | We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and clickstream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of <40 ms serving 1000 rpm. | [
"['Venkatesh C' 'Harshit Oberoi' 'Anil Goyal' 'Nikhil Sikka']"
]
|
null | null | 2404.16560 | null | null | http://arxiv.org/pdf/2404.16560v1 | 2024-04-25T12:16:58Z | 2024-04-25T12:16:58Z | Automated Model Selection for Generalized Linear Models | In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model. | [
"['Benjamin Schwendinger' 'Florian Schwendinger' 'Laura Vana-Gür']"
]
|
null | null | 2404.16564 | null | null | http://arxiv.org/abs/2404.16564v1 | 2024-04-25T12:27:22Z | 2024-04-25T12:27:22Z | Deep learning-based blind image super-resolution with iterative kernel
reconstruction and noise estimation | Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using na"ive deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur. | [
"['Hasan F. Ates' 'Suleyman Yildirim' 'Bahadir K. Gunturk']"
]
|
null | null | 2404.16616 | null | null | http://arxiv.org/pdf/2404.16616v1 | 2024-04-25T13:56:05Z | 2024-04-25T13:56:05Z | Robust Capped lp-Norm Support Vector Ordinal Regression | Ordinal regression is a specialized supervised problem where the labels show an inherent order. The order distinguishes it from normal multi-class problem. Support Vector Ordinal Regression, as an outstanding ordinal regression model, is widely used in many ordinal regression tasks. However, like most supervised learning algorithms, the design of SVOR is based on the assumption that the training data are real and reliable, which is difficult to satisfy in real-world data. In many practical applications, outliers are frequently present in the training set, potentially leading to misguide the learning process, such that the performance is non-optimal. In this paper, we propose a novel capped $ell_{p}$-norm loss function that is theoretically robust to both light and heavy outliers. The capped $ell_{p}$-norm loss can help the model detect and eliminate outliers during training process. Adhering to this concept, we introduce a new model, Capped $ell_{p}$-Norm Support Vector Ordinal Regression(CSVOR), that is robust to outliers. CSVOR uses a weight matrix to detect and eliminate outliers during the training process to improve the robustness to outliers. Moreover, a Re-Weighted algorithm algorithm which is illustrated convergence by our theoretical results is proposed to effectively minimize the corresponding problem. Extensive experimental results demonstrate that our model outperforms state-of-the-art(SOTA) methods, particularly in the presence of outliers. | [
"['Haorui Xiang' 'Zhichang Wu' 'Guoxu Li' 'Rong Wang' 'Feiping Nie'\n 'Xuelong Li']"
]
|
null | null | 2404.16621 | null | null | http://arxiv.org/pdf/2404.16621v1 | 2024-04-25T14:06:37Z | 2024-04-25T14:06:37Z | Hippocrates: An Open-Source Framework for Advancing Large Language
Models in Healthcare | The integration of Large Language Models (LLMs) into healthcare promises to transform medical diagnostics, research, and patient care. Yet, the progression of medical LLMs faces obstacles such as complex training requirements, rigorous evaluation demands, and the dominance of proprietary models that restrict academic exploration. Transparent, comprehensive access to LLM resources is essential for advancing the field, fostering reproducibility, and encouraging innovation in healthcare AI. We present Hippocrates, an open-source LLM framework specifically developed for the medical domain. In stark contrast to previous efforts, it offers unrestricted access to its training datasets, codebase, checkpoints, and evaluation protocols. This open approach is designed to stimulate collaborative research, allowing the community to build upon, refine, and rigorously evaluate medical LLMs within a transparent ecosystem. Also, we introduce Hippo, a family of 7B models tailored for the medical domain, fine-tuned from Mistral and LLaMA2 through continual pre-training, instruction tuning, and reinforcement learning from human and AI feedback. Our models outperform existing open medical LLMs models by a large-margin, even surpassing models with 70B parameters. Through Hippocrates, we aspire to unlock the full potential of LLMs not just to advance medical knowledge and patient care but also to democratize the benefits of AI research in healthcare, making them available across the globe. | [
"['Emre Can Acikgoz' 'Osman Batur İnce' 'Rayene Bench' 'Arda Anıl Boz'\n 'İlker Kesen' 'Aykut Erdem' 'Erkut Erdem']"
]
|
null | null | 2404.16630 | null | null | http://arxiv.org/pdf/2404.16630v1 | 2024-04-25T14:17:34Z | 2024-04-25T14:17:34Z | Legal Aspects for Software Developers Interested in Generative AI
Applications | Recent successes in Generative Artificial Intelligence (GenAI) have led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into products, a task typically conducted by software developers. Such product development always comes with a certain risk of liability. Within this article, we want to shed light on the current state of two such risks: data protection and copyright. Both aspects are crucial for GenAI. This technology deals with data for both model training and generated output. We summarize key aspects regarding our current knowledge that every software developer involved in product development using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims. | [
"['Steffen Herbold' 'Brian Valerius' 'Anamaria Mojica-Hanke' 'Isabella Lex'\n 'Joel Mittel']"
]
|
null | null | 2404.16638 | null | null | http://arxiv.org/pdf/2404.16638v1 | 2024-04-25T14:26:53Z | 2024-04-25T14:26:53Z | Privacy-Preserving Statistical Data Generation: Application to Sepsis
Detection | The biomedical field is among the sectors most impacted by the increasing regulation of Artificial Intelligence (AI) and data protection legislation, given the sensitivity of patient information. However, the rise of synthetic data generation methods offers a promising opportunity for data-driven technologies. In this study, we propose a statistical approach for synthetic data generation applicable in classification problems. We assess the utility and privacy implications of synthetic data generated by Kernel Density Estimator and K-Nearest Neighbors sampling (KDE-KNN) within a real-world context, specifically focusing on its application in sepsis detection. The detection of sepsis is a critical challenge in clinical practice due to its rapid progression and potentially life-threatening consequences. Moreover, we emphasize the benefits of KDE-KNN compared to current synthetic data generation methodologies. Additionally, our study examines the effects of incorporating synthetic data into model training procedures. This investigation provides valuable insights into the effectiveness of synthetic data generation techniques in mitigating regulatory constraints within the biomedical field. | [
"['Eric Macias-Fassio' 'Aythami Morales' 'Cristina Pruenza'\n 'Julian Fierrez']"
]
|
null | null | 2404.16647 | null | null | http://arxiv.org/pdf/2404.16647v1 | 2024-04-25T14:36:00Z | 2024-04-25T14:36:00Z | Application of RESNET50 Convolution Neural Network for the Extraction of
Optical Parameters in Scattering Media | Estimation of the optical properties of scattering media such as tissue is important in diagnostics as well as in the development of techniques to image deeper. As light penetrates the sample scattering events occur that alter the propagation direction of the photons in a random manner leading degradation of image quality. The distribution of the scattered light does, however, give a measure of the optical properties such as the reduced scattering coefficient and the absorption coefficient. Unfortunately, inverting scattering patterns to recover the optical properties is not simple, especially in the regime where the light is partially randomized. Machine learning has been proposed by several authors as a means of recovering these properties from either the back scattered or the transmitted light. In the present paper, we train a general purpose convolutional neural network RESNET 50 with simulated data based on Monte Carlo simulations. We show that compared with previous work our approach gives comparable or better reconstruction accuracy with training on a much smaller dataset. Moreover, by training on multiple parameters such as the intensity distribution at multiple planes or the exit angle and spatial distribution one achieves improved performance compared to training on a single input such as the intensity distribution captured at the sample surface. While our approach gives good parameter reconstruction, we identify factors that limit the accuracy of the recovered properties, particularly the absorption coefficient. In the light of these limitations, we suggest how the present approach may be enhanced for even better performance. | [
"['Bowen Deng' 'Yihan Zhang' 'Andrew Parkes' 'Alex Bentley' 'Amanda Wright'\n 'Michael Pound' 'Michael Somekh']"
]
|
null | null | 2404.16656 | null | null | http://arxiv.org/pdf/2404.16656v1 | 2024-04-25T14:48:29Z | 2024-04-25T14:48:29Z | A Self-Organizing Clustering System for Unsupervised Distribution Shift
Detection | Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model. Classic learning tools are often vulnerable to perturbations of the input covariates, and are sensitive to outliers and noise, and some tools are based on rigid algebraic assumptions. Distribution shifts are frequently occurring due to changes in raw materials for production, seasonality, a different user base, or even adversarial attacks. Therefore, there is a need for more effective distribution shift detection techniques. In this work, we propose a continual learning framework for monitoring and detecting distribution changes. We explore the problem in a latent space generated by a bio-inspired self-organizing clustering and statistical aspects of the latent space. In particular, we investigate the projections made by two topology-preserving maps: the Self-Organizing Map and the Scale Invariant Map. Our method can be applied in both a supervised and an unsupervised context. We construct the assessment of changes in the data distribution as a comparison of Gaussian signals, making the proposed method fast and robust. We compare it to other unsupervised techniques, specifically Principal Component Analysis (PCA) and Kernel-PCA. Our comparison involves conducting experiments using sequences of images (based on MNIST and injected shifts with adversarial samples), chemical sensor measurements, and the environmental variable related to ozone levels. The empirical study reveals the potential of the proposed approach. | [
"['Sebastián Basterrech' 'Line Clemmensen' 'Gerardo Rubino']"
]
|
null | null | 2404.16660 | null | null | http://arxiv.org/pdf/2404.16660v1 | 2024-04-25T14:56:32Z | 2024-04-25T14:56:32Z | Benchmarking Mobile Device Control Agents across Diverse Configurations | Developing autonomous agents for mobile devices can significantly enhance user interactions by offering increased efficiency and accessibility. However, despite the growing interest in mobile device control agents, the absence of a commonly adopted benchmark makes it challenging to quantify scientific progress in this area. In this work, we introduce B-MoCA: a novel benchmark designed specifically for evaluating mobile device control agents. To create a realistic benchmark, we develop B-MoCA based on the Android operating system and define 60 common daily tasks. Importantly, we incorporate a randomization feature that changes various aspects of mobile devices, including user interface layouts and language settings, to assess generalization performance. We benchmark diverse agents, including agents employing large language models (LLMs) or multi-modal LLMs as well as agents trained from scratch using human expert demonstrations. While these agents demonstrate proficiency in executing straightforward tasks, their poor performance on complex tasks highlights significant opportunities for future research to enhance their effectiveness. Our source code is publicly available at https://b-moca.github.io. | [
"['Juyong Lee' 'Taywon Min' 'Minyong An' 'Changyeon Kim' 'Kimin Lee']"
]
|
null | null | 2404.16663 | null | null | http://arxiv.org/pdf/2404.16663v3 | 2024-05-06T06:50:15Z | 2024-04-25T15:04:27Z | Formal Specification, Assessment, and Enforcement of Fairness for
Generative AIs | Reinforcing or even exacerbating societal biases and inequalities will increase significantly as generative AI increasingly produces useful artifacts, from text to images and beyond, for the real world. We address these issues by formally characterizing the notion of fairness for generative AI as a basis for monitoring and enforcing fairness. We define two levels of fairness using the notion of infinite sequences of abstractions of AI-generated artifacts such as text or images. The first is the fairness demonstrated on the generated sequences, which is evaluated only on the outputs while agnostic to the prompts and models used. The second is the inherent fairness of the generative AI model, which requires that fairness be manifested when input prompts are neutral, that is, they do not explicitly instruct the generative AI to produce a particular type of output. We also study relative intersectional fairness to counteract the combinatorial explosion of fairness when considering multiple categories together with lazy fairness enforcement. Finally, fairness monitoring and enforcement are tested against some current generative AI models. | [
"['Chih-Hong Cheng' 'Changshun Wu' 'Harald Ruess' 'Xingyu Zhao'\n 'Saddek Bensalem']"
]
|
null | null | 2404.16676 | null | null | http://arxiv.org/pdf/2404.16676v1 | 2024-04-25T15:25:30Z | 2024-04-25T15:25:30Z | Multilayer Correlation Clustering | In this paper, we establish Multilayer Correlation Clustering, a novel generalization of Correlation Clustering (Bansal et al., FOCS '02) to the multilayer setting. In this model, we are given a series of inputs of Correlation Clustering (called layers) over the common set $V$. The goal is then to find a clustering of $V$ that minimizes the $ell_p$-norm ($pgeq 1$) of the disagreements vector, which is defined as the vector (with dimension equal to the number of layers), each element of which represents the disagreements of the clustering on the corresponding layer. For this generalization, we first design an $O(Llog n)$-approximation algorithm, where $L$ is the number of layers, based on the well-known region growing technique. We then study an important special case of our problem, namely the problem with the probability constraint. For this case, we first give an $(alpha+2)$-approximation algorithm, where $alpha$ is any possible approximation ratio for the single-layer counterpart. For instance, we can take $alpha=2.5$ in general (Ailon et al., JACM '08) and $alpha=1.73+epsilon$ for the unweighted case (Cohen-Addad et al., FOCS '23). Furthermore, we design a $4$-approximation algorithm, which improves the above approximation ratio of $alpha+2=4.5$ for the general probability-constraint case. Computational experiments using real-world datasets demonstrate the effectiveness of our proposed algorithms. | [
"['Atsushi Miyauchi' 'Florian Adriaens' 'Francesco Bonchi' 'Nikolaj Tatti']"
]
|
null | null | 2404.16706 | null | null | http://arxiv.org/pdf/2404.16706v3 | 2024-05-06T16:19:09Z | 2024-04-25T16:11:46Z | Efficient and Near-Optimal Noise Generation for Streaming Differential
Privacy | In the task of differentially private (DP) continual counting, we receive a stream of increments and our goal is to output an approximate running total of these increments, without revealing too much about any specific increment. Despite its simplicity, differentially private continual counting has attracted significant attention both in theory and in practice. Existing algorithms for differentially private continual counting are either inefficient in terms of their space usage or add an excessive amount of noise, inducing suboptimal utility. The most practical DP continual counting algorithms add carefully correlated Gaussian noise to the values. The task of choosing the covariance for this noise can be expressed in terms of factoring the lower-triangular matrix of ones (which computes prefix sums). We present two approaches from this class (for different parameter regimes) that achieve near-optimal utility for DP continual counting and only require logarithmic or polylogarithmic space (and time). Our first approach is based on a space-efficient streaming matrix multiplication algorithm for a class of Toeplitz matrices. We show that to instantiate this algorithm for DP continual counting, it is sufficient to find a low-degree rational function that approximates the square root on a circle in the complex plane. We then apply and extend tools from approximation theory to achieve this. We also derive efficient closed-forms for the objective function for arbitrarily many steps, and show direct numerical optimization yields a highly practical solution to the problem. Our second approach combines our first approach with a recursive construction similar to the binary tree mechanism. | [
"['Krishnamurthy Dvijotham' 'H. Brendan McMahan' 'Krishna Pillutla'\n 'Thomas Steinke' 'Abhradeep Thakurta']"
]
|
null | null | 2404.16710 | null | null | http://arxiv.org/pdf/2404.16710v2 | 2024-04-29T15:02:36Z | 2024-04-25T16:20:23Z | LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding | We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. | [
"['Mostafa Elhoushi' 'Akshat Shrivastava' 'Diana Liskovich' 'Basil Hosmer'\n 'Bram Wasti' 'Liangzhen Lai' 'Anas Mahmoud' 'Bilge Acun'\n 'Saurabh Agarwal' 'Ahmed Roman' 'Ahmed A Aly' 'Beidi Chen'\n 'Carole-Jean Wu']"
]
|
null | null | 2404.16718 | null | null | http://arxiv.org/pdf/2404.16718v1 | 2024-04-25T16:30:30Z | 2024-04-25T16:30:30Z | Features Fusion for Dual-View Mammography Mass Detection | Detection of malignant lesions on mammography images is extremely important for early breast cancer diagnosis. In clinical practice, images are acquired from two different angles, and radiologists can fully utilize information from both views, simultaneously locating the same lesion. However, for automatic detection approaches such information fusion remains a challenge. In this paper, we propose a new model called MAMM-Net, which allows the processing of both mammography views simultaneously by sharing information not only on an object level, as seen in existing works, but also on a feature level. MAMM-Net's key component is the Fusion Layer, based on deformable attention and designed to increase detection precision while keeping high recall. Our experiments show superior performance on the public DDSM dataset compared to the previous state-of-the-art model, while introducing new helpful features such as lesion annotation on pixel-level and classification of lesions malignancy. | [
"['Arina Varlamova' 'Valery Belotsky' 'Grigory Novikov' 'Anton Konushin'\n 'Evgeny Sidorov']"
]
|
null | null | 2404.16721 | null | null | http://arxiv.org/pdf/2404.16721v1 | 2024-04-25T16:33:19Z | 2024-04-25T16:33:19Z | Distilling Privileged Information for Dubins Traveling Salesman Problems
with Neighborhoods | This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase trains an adaptation network to solve problems independently of privileged information. Before the first learning phase, a parameter initialization technique using the demonstration data was also devised to enhance training efficiency. The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and RL with demonstration schemes, most of which fail to sense all the task points. | [
"['Min Kyu Shin' 'Su-Jeong Park' 'Seung-Keol Ryu' 'Heeyeon Kim'\n 'Han-Lim Choi']"
]
|
null | null | 2404.16724 | null | null | http://arxiv.org/pdf/2404.16724v1 | 2024-04-25T16:37:58Z | 2024-04-25T16:37:58Z | Tverberg's theorem and multi-class support vector machines | We show how, using linear-algebraic tools developed to prove Tverberg's theorem in combinatorial geometry, we can design new models of multi-class support vector machines (SVMs). These supervised learning protocols require fewer conditions to classify sets of points, and can be computed using existing binary SVM algorithms in higher-dimensional spaces, including soft-margin SVM algorithms. We describe how the theoretical guarantees of standard support vector machines transfer to these new classes of multi-class support vector machines. We give a new simple proof of a geometric characterization of support vectors for largest margin SVMs by Veelaert. | [
"['Pablo Soberón']"
]
|
null | null | 2404.16726 | null | null | http://arxiv.org/pdf/2404.16726v2 | 2024-04-29T22:50:42Z | 2024-04-25T16:39:32Z | History repeats Itself: A Baseline for Temporal Knowledge Graph
Forecasting | Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art. | [
"['Julia Gastinger' 'Christian Meilicke' 'Federico Errica' 'Timo Sztyler'\n 'Anett Schuelke' 'Heiner Stuckenschmidt']"
]
|
null | null | 2404.16767 | null | null | http://arxiv.org/pdf/2404.16767v2 | 2024-05-29T15:52:20Z | 2024-04-25T17:20:45Z | REBEL: Reinforcement Learning via Regressing Relative Rewards | While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g. value networks, clipping), and is notorious for its sensitivity to the precise implementation of these components. In response, we take a step back and ask what a minimalist RL algorithm for the era of generative models would look like. We propose REBEL, an algorithm that cleanly reduces the problem of policy optimization to regressing the relative reward between two completions to a prompt in terms of the policy, enabling strikingly lightweight implementation. In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL, which allows us to match the strongest known theoretical guarantees in terms of convergence and sample complexity in the RL literature. REBEL can also cleanly incorporate offline data and be extended to handle the intransitive preferences we frequently see in practice. Empirically, we find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO, all while being simpler to implement and more computationally efficient than PPO. When fine-tuning Llama-3-8B-Instruct, REBEL achieves strong performance in AlpacaEval 2.0, MT-Bench, and Open LLM Leaderboard. | [
"['Zhaolin Gao' 'Jonathan D. Chang' 'Wenhao Zhan' 'Owen Oertell'\n 'Gokul Swamy' 'Kianté Brantley' 'Thorsten Joachims' 'J. Andrew Bagnell'\n 'Jason D. Lee' 'Wen Sun']"
]
|
null | null | 2404.16779 | null | null | http://arxiv.org/pdf/2404.16779v1 | 2024-04-25T17:28:33Z | 2024-04-25T17:28:33Z | DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks | The success of many RL techniques heavily relies on human-engineered dense rewards, which typically demand substantial domain expertise and extensive trial and error. In our work, we propose DrS (Dense reward learning from Stages), a novel approach for learning reusable dense rewards for multi-stage tasks in a data-driven manner. By leveraging the stage structures of the task, DrS learns a high-quality dense reward from sparse rewards and demonstrations if given. The learned rewards can be textit{reused} in unseen tasks, thus reducing the human effort for reward engineering. Extensive experiments on three physical robot manipulation task families with 1000+ task variants demonstrate that our learned rewards can be reused in unseen tasks, resulting in improved performance and sample efficiency of RL algorithms. The learned rewards even achieve comparable performance to human-engineered rewards on some tasks. See our project page (https://sites.google.com/view/iclr24drs) for more details. | [
"['Tongzhou Mu' 'Minghua Liu' 'Hao Su']"
]
|
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