categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.08704 | null | null | http://arxiv.org/pdf/2405.08704v1 | 2024-05-14T15:42:55Z | 2024-05-14T15:42:55Z | Full Line Code Completion: Bringing AI to Desktop | In recent years, several industrial solutions for the problem of multi-token code completion have appeared, each making a great advance in the area but mostly focusing on cloud-based runtime and avoiding working on the end user's device. In this work, we describe our approach for building a multi-token code completion feature for the JetBrains' IntelliJ Platform, which we call Full Line Code Completion. The feature suggests only syntactically correct code and works fully locally, i.e., data querying and the generation of suggestions happens on the end user's machine. We share important time and memory-consumption restrictions, as well as design principles that a code completion engine should satisfy. Working entirely on the end user's device, our code completion engine enriches user experience while being not only fast and compact but also secure. We share a number of useful techniques to meet the stated development constraints and also describe offline and online evaluation pipelines that allowed us to make better decisions. Our online evaluation shows that the usage of the tool leads to 1.5 times more code in the IDE being produced by code completion. The described solution was initially started with the help of researchers and was bundled into two JetBrains' IDEs - PyCharm Pro and DataSpell - at the end of 2023, so we believe that this work is useful for bridging academia and industry, providing researchers with the knowledge of what happens when complex research-based solutions are integrated into real products. | [
"['Anton Semenkin' 'Vitaliy Bibaev' 'Yaroslav Sokolov' 'Kirill Krylov'\n 'Alexey Kalina' 'Anna Khannanova' 'Danila Savenkov' 'Darya Rovdo'\n 'Igor Davidenko' 'Kirill Karnaukhov' 'Maxim Vakhrushev'\n 'Mikhail Kostyukov' 'Mikhail Podvitskii' 'Petr Surkov' 'Yaroslav Golubev'\n 'Nikita Povarov' 'Timofey Bryksin']"
]
|
null | null | 2405.08707 | null | null | http://arxiv.org/pdf/2405.08707v1 | 2024-05-14T15:48:36Z | 2024-05-14T15:48:36Z | Beyond Scaling Laws: Understanding Transformer Performance with
Associative Memory | Increasing the size of a Transformer model does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, improved generalization ability occurs as the model memorizes the training samples. We present a theoretical framework that sheds light on the memorization process and performance dynamics of transformer-based language models. We model the behavior of Transformers with associative memories using Hopfield networks, such that each transformer block effectively conducts an approximate nearest-neighbor search. Based on this, we design an energy function analogous to that in the modern continuous Hopfield network which provides an insightful explanation for the attention mechanism. Using the majorization-minimization technique, we construct a global energy function that captures the layered architecture of the Transformer. Under specific conditions, we show that the minimum achievable cross-entropy loss is bounded from below by a constant approximately equal to 1. We substantiate our theoretical results by conducting experiments with GPT-2 on various data sizes, as well as training vanilla Transformers on a dataset of 2M tokens. | [
"['Xueyan Niu' 'Bo Bai' 'Lei Deng' 'Wei Han']"
]
|
null | null | 2405.08711 | null | null | http://arxiv.org/pdf/2405.08711v1 | 2024-05-14T15:51:52Z | 2024-05-14T15:51:52Z | Data-driven Force Observer for Human-Robot Interaction with Series
Elastic Actuators using Gaussian Processes | Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic components. While observer-based methods have been shown to improve these estimates, they rely on accurate models of the system, which are challenging to obtain in complex operating environments. In this work, we overcome this issue by learning the unknown dynamics components using Gaussian process (GP) regression. By employing the learned model in a Bayesian filtering framework, we improve the estimation accuracy and additionally obtain an observer that explicitly considers local model uncertainty in the confidence measure of the state estimate. Furthermore, we derive guaranteed estimation error bounds, thus, facilitating the use in safety-critical applications. We demonstrate the effectiveness of the proposed approach experimentally in a human-exoskeleton interaction scenario. | [
"['Samuel Tesfazgi' 'Markus Keßler' 'Emilio Trigili' 'Armin Lederer'\n 'Sandra Hirche']"
]
|
null | null | 2405.08719 | null | null | http://arxiv.org/pdf/2405.08719v1 | 2024-05-14T16:04:39Z | 2024-05-14T16:04:39Z | Addressing Misspecification in Simulation-based Inference through
Data-driven Calibration | Driven by steady progress in generative modeling, simulation-based inference (SBI) has enabled inference over stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI's reliability. This work introduces robust posterior estimation (ROPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements. We formalize the misspecification gap as the solution of an optimal transport problem between learned representations of real-world and simulated observations. Assuming the prior distribution over the parameters of interest is known and well-specified, our method offers a controllable balance between calibrated uncertainty and informative inference under all possible misspecifications of the simulator. Our empirical results on four synthetic tasks and two real-world problems demonstrate that ROPE outperforms baselines and consistently returns informative and calibrated credible intervals. | [
"['Antoine Wehenkel' 'Juan L. Gamella' 'Ozan Sener' 'Jens Behrmann'\n 'Guillermo Sapiro' 'Marco Cuturi' 'Jörn-Henrik Jacobsen']"
]
|
null | null | 2405.08740 | null | null | http://arxiv.org/pdf/2405.08740v3 | 2024-06-02T10:15:53Z | 2024-05-14T16:30:03Z | Reinformer: Max-Return Sequence Modeling for Offline RL | As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as sequence modeling that conditions on the hindsight information including returns, goal or future trajectory. Although promising, this supervised paradigm overlooks the core objective of RL that maximizes the return. This overlook directly leads to the lack of trajectory stitching capability that affects the sequence model learning from sub-optimal data. In this work, we introduce the concept of max-return sequence modeling which integrates the goal of maximizing returns into existing sequence models. We propose Reinforced Transformer (Reinformer), indicating the sequence model is reinforced by the RL objective. Reinformer additionally incorporates the objective of maximizing returns in the training phase, aiming to predict the maximum future return within the distribution. During inference, this in-distribution maximum return will guide the selection of optimal actions. Empirically, Reinformer is competitive with classical RL methods on the D4RL benchmark and outperforms state-of-the-art sequence model particularly in trajectory stitching ability. Code is public at https://github.com/Dragon-Zhuang/Reinformer. | [
"['Zifeng Zhuang' 'Dengyun Peng' 'Jinxin Liu' 'Ziqi Zhang' 'Donglin Wang']"
]
|
null | null | 2405.08754 | null | null | http://arxiv.org/abs/2405.08754v1 | 2024-05-14T16:40:06Z | 2024-05-14T16:40:06Z | Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement
Learning Approach | GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increased exponentially over the past decades, it has become increasingly difficult for a single program to fully utilize them. As a consequence, the industry has started supporting several resource partitioning features in order to improve the resource utilization by co-scheduling multiple programs on the same GPU die at the same time. Driven by the technological trend, this paper focuses on hierarchical resource partitioning on modern GPUs, and as an example, we utilize a combination of two different features available on recent NVIDIA GPUs in a hierarchical manner: MPS (Multi-Process Service), a finer-grained logical partitioning; and MIG (Multi-Instance GPU), a coarse-grained physical partitioning. We propose a method for comprehensively co-optimizing the setup of hierarchical partitioning and the selection of co-scheduling groups from a given set of jobs, based on reinforcement learning using their profiles. Our thorough experimental results demonstrate that our approach can successfully set up job concurrency, partitioning, and co-scheduling group selections simultaneously. This results in a maximum throughput improvement by a factor of 1.87 compared to the time-sharing scheduling. | [
"['Urvij Saroliya' 'Eishi Arima' 'Dai Liu' 'Martin Schulz']"
]
|
null | null | 2405.08755 | null | null | http://arxiv.org/pdf/2405.08755v2 | 2024-05-26T06:06:08Z | 2024-05-14T16:40:37Z | Distributed Threat Intelligence at the Edge Devices: A Large Language
Model-Driven Approach | With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decentralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning techniques such as the in-context learning feature of Large Language Models (LLMs), represents a promising paradigm for enhancing cybersecurity on resource-constrained edge devices. This approach involves the deployment of lightweight machine learning models directly onto edge devices to analyze local data streams, such as network traffic and system logs, in real-time. Additionally, distributing computational tasks to an edge server reduces latency and improves responsiveness while also enhancing privacy by processing sensitive data locally. LLM servers can enable these edge servers to autonomously adapt to evolving threats and attack patterns, continuously updating their models to improve detection accuracy and reduce false positives. Furthermore, collaborative learning mechanisms facilitate peer-to-peer secure and trustworthy knowledge sharing among edge devices, enhancing the collective intelligence of the network and enabling dynamic threat mitigation measures such as device quarantine in response to detected anomalies. The scalability and flexibility of this approach make it well-suited for diverse and evolving network environments, as edge devices only send suspicious information such as network traffic and system log changes, offering a resilient and efficient solution to combat emerging cyber threats at the network edge. Thus, our proposed framework can improve edge computing security by providing better security in cyber threat detection and mitigation by isolating the edge devices from the network. | [
"['Syed Mhamudul Hasan' 'Alaa M. Alotaibi' 'Sajedul Talukder'\n 'Abdur R. Shahid']"
]
|
null | null | 2405.08756 | null | null | http://arxiv.org/pdf/2405.08756v1 | 2024-05-14T16:40:45Z | 2024-05-14T16:40:45Z | Stable Inverse Reinforcement Learning: Policies from Control Lyapunov
Landscapes | Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborative control settings. A common method to solve this problem is inverse reinforcement learning (IRL), where the observed agent, e.g., a human demonstrator, is assumed to behave according to the optimization of an intrinsic cost function that reflects its intent and informs its control actions. While the framework is expressive, it is also computationally demanding and generally lacks convergence guarantees. We therefore propose a novel, stability-certified IRL approach by reformulating the cost function inference problem to learning control Lyapunov functions (CLF) from demonstrations data. By additionally exploiting closed-form expressions for associated control policies, we are able to efficiently search the space of CLFs by observing the attractor landscape of the induced dynamics. For the construction of the inverse optimal CLFs, we use a Sum of Squares and formulate a convex optimization problem. We present a theoretical analysis of the optimality properties provided by the CLF and evaluate our approach using both simulated and real-world data. | [
"['Samuel Tesfazgi' 'Leonhard Sprandl' 'Armin Lederer' 'Sandra Hirche']"
]
|
null | null | 2405.08766 | null | null | http://arxiv.org/pdf/2405.08766v1 | 2024-05-14T16:59:20Z | 2024-05-14T16:59:20Z | Energy-based Hopfield Boosting for Out-of-Distribution Detection | Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy (MHE) to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to concentrate on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 metric from 2.28 to 0.92 on CIFAR-10 and from 11.76 to 7.94 on CIFAR-100. | [
"['Claus Hofmann' 'Simon Schmid' 'Bernhard Lehner' 'Daniel Klotz'\n 'Sepp Hochreiter']"
]
|
null | null | 2405.08768 | null | null | http://arxiv.org/pdf/2405.08768v1 | 2024-05-14T17:00:43Z | 2024-05-14T17:00:43Z | EfficientTrain++: Generalized Curriculum Learning for Efficient Visual
Backbone Training | The superior performance of modern visual backbones usually comes with a costly training procedure. We contribute to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these aspects and design curriculum schedules with tailored search algorithms. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. It reduces the training time of a wide variety of popular models by 1.5-3.0x on ImageNet-1K/22K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE). | [
"['Yulin Wang' 'Yang Yue' 'Rui Lu' 'Yizeng Han' 'Shiji Song' 'Gao Huang']"
]
|
null | null | 2405.08779 | null | null | http://arxiv.org/pdf/2405.08779v1 | 2024-05-14T17:13:50Z | 2024-05-14T17:13:50Z | Jacobian Regularizer-based Neural Granger Causality | With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear relationships. However, the existing framework of neural Granger causality has several limitations. It requires the construction of separate predictive models for each target variable, and the relationship depends on the sparsity on the weights of the first layer, resulting in challenges in effectively modeling complex relationships between variables as well as unsatisfied estimation accuracy of Granger causality. Moreover, most of them cannot grasp full-time Granger causality. To address these drawbacks, we propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach, a straightforward yet highly effective method for learning multivariate summary Granger causality and full-time Granger causality by constructing a single model for all target variables. Specifically, our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer, which can be subsequently represented as the weighted causal matrix in the post-hoc analysis. Extensive experiments show that our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality while maintaining lower model complexity and high scalability. | [
"['Wanqi Zhou' 'Shuanghao Bai' 'Shujian Yu' 'Qibin Zhao' 'Badong Chen']"
]
|
null | null | 2405.08790 | null | null | http://arxiv.org/pdf/2405.08790v1 | 2024-05-14T17:38:17Z | 2024-05-14T17:38:17Z | Kolmogorov-Arnold Networks (KANs) for Time Series Analysis | This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing them to learn activation patterns dynamically. We demonstrate that KANs outperforms conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with considerably fewer number of learnable parameters. We also provide an ablation study of KAN-specific parameters impact on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics. | [
"['Cristian J. Vaca-Rubio' 'Luis Blanco' 'Roberto Pereira' 'Màrius Caus']"
]
|
null | null | 2405.08792 | null | null | http://arxiv.org/pdf/2405.08792v1 | 2024-05-14T17:41:07Z | 2024-05-14T17:41:07Z | Towards Enhanced RAC Accessibility: Leveraging Datasets and LLMs | This paper explores the potential of large language models (LLMs) to make the Aeronautical Regulations of Colombia (RAC) more accessible. Given the complexity and extensive technicality of the RAC, this study introduces a novel approach to simplifying these regulations for broader understanding. By developing the first-ever RAC database, which contains 24,478 expertly labeled question-and-answer pairs, and fine-tuning LLMs specifically for RAC applications, the paper outlines the methodology for dataset assembly, expert-led annotation, and model training. Utilizing the Gemma1.1 2b model along with advanced techniques like Unsloth for efficient VRAM usage and flash attention mechanisms, the research aims to expedite training processes. This initiative establishes a foundation to enhance the comprehensibility and accessibility of RAC, potentially benefiting novices and reducing dependence on expert consultations for navigating the aviation industry's regulatory landscape. You can visit the dataset (https://huggingface.co/somosnlp/gemma-1.1-2b-it_ColombiaRAC_FullyCurated_format_chatML_V1) and the model (https://huggingface.co/datasets/somosnlp/ColombiaRAC_FullyCurated) here. | [
"['Edison Jair Bejarano Sepulveda' 'Nicolai Potes Hector'\n 'Santiago Pineda Montoya' 'Felipe Ivan Rodriguez' 'Jaime Enrique Orduy'\n 'Alec Rosales Cabezas' 'Danny Traslaviña Navarrete'\n 'Sergio Madrid Farfan']"
]
|
null | null | 2405.08793 | null | null | http://arxiv.org/pdf/2405.08793v1 | 2024-05-14T17:41:55Z | 2024-05-14T17:41:55Z | A Brief Introduction to Causal Inference in Machine Learning | This is a lecture note produced for DS-GA 3001.003 "Special Topics in DS - Causal Inference in Machine Learning" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular, this course focuses on introducing such students to expand their view and knowledge of machine learning to incorporate causal reasoning, as this aspect is at the core of so-called out-of-distribution generalization (or lack thereof.) | [
"['Kyunghyun Cho']"
]
|
null | null | 2405.08801 | null | null | http://arxiv.org/pdf/2405.08801v2 | 2024-05-15T17:46:34Z | 2024-05-14T17:49:18Z | Prospects of Privacy Advantage in Quantum Machine Learning | Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the increasing success of recovering input data from the gradients of classical models, this study addresses a central question: How hard is it to recover the input data from the gradients of quantum machine learning models? Focusing on variational quantum circuits (VQC) as learning models, we uncover the crucial role played by the dynamical Lie algebra (DLA) of the VQC ansatz in determining privacy vulnerabilities. While the DLA has previously been linked to the classical simulatability and trainability of VQC models, this work, for the first time, establishes its connection to the privacy of VQC models. In particular, we show that properties conducive to the trainability of VQCs, such as a polynomial-sized DLA, also facilitate the extraction of detailed snapshots of the input. We term this a weak privacy breach, as the snapshots enable training VQC models for distinct learning tasks without direct access to the original input. Further, we investigate the conditions for a strong privacy breach where the original input data can be recovered from these snapshots by classical or quantum-assisted polynomial time methods. We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models. Our findings thus play a crucial role in detailing the prospects of quantum privacy advantage by guiding the requirements for designing quantum machine learning models that balance trainability with robust privacy protection. | [
"['Jamie Heredge' 'Niraj Kumar' 'Dylan Herman' 'Shouvanik Chakrabarti'\n 'Romina Yalovetzky' 'Shree Hari Sureshbabu' 'Changhao Li' 'Marco Pistoia']"
]
|
null | null | 2405.08813 | null | null | http://arxiv.org/pdf/2405.08813v2 | 2024-06-14T17:59:34Z | 2024-05-14T17:59:02Z | CinePile: A Long Video Question Answering Dataset and Benchmark | Current datasets for long-form video understanding often fall short of providing genuine long-form comprehension challenges, as many tasks derived from these datasets can be successfully tackled by analyzing just one or a few random frames from a video. To address this issue, we present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding. This paper details our innovative approach for creating a question-answer dataset, utilizing advanced LLMs with human-in-the-loop and building upon human-generated raw data. Our comprehensive dataset comprises 305,000 multiple-choice questions (MCQs), covering various visual and multimodal aspects, including temporal comprehension, understanding human-object interactions, and reasoning about events or actions within a scene. Additionally, we evaluate recent video-centric LLMs, both open-source and proprietary, on the test split of our dataset. The findings reveal that even state-of-the-art video-centric LLMs significantly lag behind human performance in these tasks, highlighting the complexity and challenge inherent in video understanding. The dataset is available at https://hf.co/datasets/tomg-group-umd/cinepile | [
"['Ruchit Rawal' 'Khalid Saifullah' 'Ronen Basri' 'David Jacobs'\n 'Gowthami Somepalli' 'Tom Goldstein']"
]
|
null | null | 2405.08825 | null | null | http://arxiv.org/pdf/2405.08825v1 | 2024-05-12T17:57:25Z | 2024-05-12T17:57:25Z | Thermodynamic limit in learning period three | A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? We consider learning period three with random neural networks and report the universal property associated with it. We first show that the trained networks have a thermodynamic limit that depends on the choice of target data and network settings. Our analysis reveals that almost all learned periods are unstable and each network has its characteristic attractors (which can even be untrained ones). Here, we propose the concept of characteristic bifurcation expressing embeddable attractors intrinsic to the network, in which the target data points and the scale of the network weights function as bifurcation parameters. In conclusion, learning period three generates various attractors through characteristic bifurcation due to the stability change in latently existing numerous unstable periods of the system. | [
"['Yuichiro Terasaki' 'Kohei Nakajima']"
]
|
null | null | 2405.08834 | null | null | http://arxiv.org/pdf/2405.08834v1 | 2024-05-14T02:42:40Z | 2024-05-14T02:42:40Z | Adversarial Machine Learning Threats to Spacecraft | Spacecraft are among the earliest autonomous systems. Their ability to function without a human in the loop have afforded some of humanity's grandest achievements. As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes-especially probabilistic ones based on machine learning. This paper aims to elucidate and demonstrate the threats that adversarial machine learning (AML) capabilities pose to spacecraft. First, an AML threat taxonomy for spacecraft is introduced. Next, we demonstrate the execution of AML attacks against spacecraft through experimental simulations using NASA's Core Flight System (cFS) and NASA's On-board Artificial Intelligence Research (OnAIR) Platform. Our findings highlight the imperative for incorporating AML-focused security measures in spacecraft that engage autonomy. | [
"['Rajiv Thummala' 'Shristi Sharma' 'Matteo Calabrese' 'Gregory Falco']"
]
|
null | null | 2405.08839 | null | null | http://arxiv.org/pdf/2405.08839v1 | 2024-05-14T07:16:56Z | 2024-05-14T07:16:56Z | PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation
using Ensemble LLMs | This paper presents our approach to the EHRSQL-2024 shared task, which aims to develop a reliable Text-to-SQL system for electronic health records. We propose two approaches that leverage large language models (LLMs) for prompting and fine-tuning to generate EHRSQL queries. In both techniques, we concentrate on bridging the gap between the real-world knowledge on which LLMs are trained and the domain specific knowledge required for the task. The paper provides the results of each approach individually, demonstrating that they achieve high execution accuracy. Additionally, we show that an ensemble approach further enhances generation reliability by reducing errors. This approach secured us 2nd place in the shared task competition. The methodologies outlined in this paper are designed to be transferable to domain-specific Text-to-SQL problems that emphasize both accuracy and reliability. | [
"['Satya K Gundabathula' 'Sriram R Kolar']"
]
|
null | null | 2405.08842 | null | null | http://arxiv.org/pdf/2405.08842v1 | 2024-05-14T07:51:55Z | 2024-05-14T07:51:55Z | Automated Deep Learning for Load Forecasting | Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases. Forecasting electricity is challenging because it depends on many external factors, such as weather and calendar variables. While regression-based models are currently effective, the emergence of new explanatory variables and the need to refine the temporality of the signals to be forecasted is encouraging the exploration of novel methodologies, in particular deep learning models. However, Deep Neural Networks (DNNs) struggle with this task due to the lack of data points and the different types of explanatory variables (e.g. integer, float, or categorical). In this paper, we explain why and how we used Automated Deep Learning (AutoDL) to find performing DNNs for load forecasting. We ended up creating an AutoDL framework called EnergyDragon by extending the DRAGON package and applying it to load forecasting. EnergyDragon automatically selects the features embedded in the DNN training in an innovative way and optimizes the architecture and the hyperparameters of the networks. We demonstrate on the French load signal that EnergyDragon can find original DNNs that outperform state-of-the-art load forecasting methods as well as other AutoDL approaches. | [
"['Julie Keisler' 'Sandra Claudel' 'Gilles Cabriel' 'Margaux Brégère']"
]
|
null | null | 2405.08843 | null | null | http://arxiv.org/pdf/2405.08843v1 | 2024-05-14T07:53:23Z | 2024-05-14T07:53:23Z | FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive
Graph-Based Learning | From a telecommunication standpoint, the surge in users and services challenges next-generation networks with escalating traffic demands and limited resources. Accurate traffic prediction can offer network operators valuable insights into network conditions and suggest optimal allocation policies. Recently, spatio-temporal forecasting, employing Graph Neural Networks (GNNs), has emerged as a promising method for cellular traffic prediction. However, existing studies, inspired by road traffic forecasting formulations, overlook the dynamic deployment and removal of base stations, requiring the GNN-based forecaster to handle an evolving graph. This work introduces a novel inductive learning scheme and a generalizable GNN-based forecasting model that can process diverse graphs of cellular traffic with one-time training. We also demonstrate that this model can be easily leveraged by transfer learning with minimal effort, making it applicable to different areas. Experimental results show up to 9.8% performance improvement compared to the state-of-the-art, especially in rare-data settings with training data reduced to below 20%. | [
"['Duc Thinh Ngo' 'Kandaraj Piamrat' 'Ons Aouedi' 'Thomas Hassan'\n 'Philippe Raipin-Parvédy']"
]
|
null | null | 2405.08852 | null | null | http://arxiv.org/pdf/2405.08852v1 | 2024-05-14T16:05:57Z | 2024-05-14T16:05:57Z | A Click-Through Rate Prediction Method Based on Cross-Importance of
Multi-Order Features | Most current click-through rate prediction(CTR)models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the importance of feature crossing; only few models are either limited to the second-order explicit feature crossing, implicitly to high-order feature crossing, or can learn the importance of high-order explicit feature crossing but fail to provide good interpretability for the model. This paper proposes a new model, FiiNet (Multiple Order Feature Interaction Importance Neural Networks). The model first uses the selective kernel network (SKNet) to explicitly construct multi-order feature crosses. It dynamically learns the importance of feature interaction combinations in a fine grained manner, increasing the attention weight of important feature cross combinations and reducing the weight of featureless crosses. To verify that the FiiNet model can dynamically learn the importance of feature interaction combinations in a fine-grained manner and improve the model's recommendation performance and interpretability, this paper compares it with many click-through rate prediction models on two real datasets, proving that the FiiNet model incorporating the selective kernel network can effectively improve the recommendation effect and provide better interpretability. FiiNet model implementations are available in PyTorch. | [
"['Hao Wang' 'Nao Li']"
]
|
null | null | 2405.08886 | null | null | http://arxiv.org/pdf/2405.08886v1 | 2024-05-14T18:05:19Z | 2024-05-14T18:05:19Z | The Pitfalls and Promise of Conformal Inference Under Adversarial
Attacks | In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used $l_{infty}$-norm bounded attack if the model is not adversarially trained, which underpins the importance of adversarial training for CP. Our paper next demonstrates that the prediction set size (PSS) of CP using adversarially trained models with AT variants is often worse than using standard AT, inspiring us to research into CP-efficient AT for improved PSS. We propose to optimize a Beta-weighting loss with an entropy minimization regularizer during AT to improve CP-efficiency, where the Beta-weighting loss is shown to be an upper bound of PSS at the population level by our theoretical analysis. Moreover, our empirical study on four image classification datasets across three popular AT baselines validates the effectiveness of the proposed Uncertainty-Reducing AT (AT-UR). | [
"['Ziquan Liu' 'Yufei Cui' 'Yan Yan' 'Yi Xu' 'Xiangyang Ji' 'Xue Liu'\n 'Antoni B. Chan']"
]
|
null | null | 2405.08888 | null | null | http://arxiv.org/pdf/2405.08888v1 | 2024-05-14T18:05:44Z | 2024-05-14T18:05:44Z | Large Language Models for Human-Machine Collaborative Particle
Accelerator Tuning through Natural Language | Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require an expert in optimisation, machine learning or a similar field to implement the algorithm for every new tuning task. In this work, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to successfully and autonomously tune a particle accelerator subsystem based on nothing more than a natural language prompt from the operator, and compare the performance of our LLM-based solution to state-of-the-art optimisation algorithms, such as Bayesian optimisation (BO) and reinforcement learning-trained optimisation (RLO). In doing so, we also show how LLMs can perform numerical optimisation of a highly non-linear real-world objective function. Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators. | [
"['Jan Kaiser' 'Annika Eichler' 'Anne Lauscher']"
]
|
null | null | 2405.08892 | null | null | http://arxiv.org/pdf/2405.08892v1 | 2024-05-14T18:10:46Z | 2024-05-14T18:10:46Z | RS-Reg: Probabilistic and Robust Certified Regression Through Randomized
Smoothing | Randomized smoothing has shown promising certified robustness against adversaries in classification tasks. Despite such success with only zeroth-order access to base models, randomized smoothing has not been extended to a general form of regression. By defining robustness in regression tasks flexibly through probabilities, we demonstrate how to establish upper bounds on input data point perturbation (using the $ell_2$ norm) for a user-specified probability of observing valid outputs. Furthermore, we showcase the asymptotic property of a basic averaging function in scenarios where the regression model operates without any constraint. We then derive a certified upper bound of the input perturbations when dealing with a family of regression models where the outputs are bounded. Our simulations verify the validity of the theoretical results and reveal the advantages and limitations of simple smoothing functions, i.e., averaging, in regression tasks. The code is publicly available at url{https://github.com/arekavandi/Certified_Robust_Regression}. | [
"['Aref Miri Rekavandi' 'Olga Ohrimenko' 'Benjamin I. P. Rubinstein']"
]
|
null | null | 2405.08911 | null | null | http://arxiv.org/pdf/2405.08911v1 | 2024-05-14T19:06:24Z | 2024-05-14T19:06:24Z | CLIP with Quality Captions: A Strong Pretraining for Vision Tasks | CLIP models perform remarkably well on zero-shot classification and retrieval tasks. But recent studies have shown that learnt representations in CLIP are not well suited for dense prediction tasks like object detection, semantic segmentation or depth estimation. More recently, multi-stage training methods for CLIP models was introduced to mitigate the weak performance of CLIP on downstream tasks. In this work, we find that simply improving the quality of captions in image-text datasets improves the quality of CLIP's visual representations, resulting in significant improvement on downstream dense prediction vision tasks. In fact, we find that CLIP pretraining with good quality captions can surpass recent supervised, self-supervised and weakly supervised pretraining methods. We show that when CLIP model with ViT-B/16 as image encoder is trained on well aligned image-text pairs it obtains 12.1% higher mIoU and 11.5% lower RMSE on semantic segmentation and depth estimation tasks over recent state-of-the-art Masked Image Modeling (MIM) pretraining methods like Masked Autoencoder (MAE). We find that mobile architectures also benefit significantly from CLIP pretraining. A recent mobile vision architecture, MCi2, with CLIP pretraining obtains similar performance as Swin-L, pretrained on ImageNet-22k for semantic segmentation task while being 6.1$times$ smaller. Moreover, we show that improving caption quality results in $10times$ data efficiency when finetuning for dense prediction tasks. | [
"['Pavan Kumar Anasosalu Vasu' 'Hadi Pouransari' 'Fartash Faghri'\n 'Oncel Tuzel']"
]
|
null | null | 2405.08917 | null | null | http://arxiv.org/pdf/2405.08917v1 | 2024-05-14T19:12:32Z | 2024-05-14T19:12:32Z | Feature Importance and Explainability in Quantum Machine Learning | Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers. | [
"['Luke Power' 'Krishnendu Guha']"
]
|
null | null | 2405.08920 | null | null | http://arxiv.org/pdf/2405.08920v2 | 2024-05-16T12:06:03Z | 2024-05-14T19:18:19Z | Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD
with Near-perfect Representation Learning | A recent study by De et al. (2022) has reported that large-scale representation learning through pre-training on a public dataset significantly enhances differentially private (DP) learning in downstream tasks, despite the high dimensionality of the feature space. To theoretically explain this phenomenon, we consider the setting of a layer-peeled model in representation learning, which results in interesting phenomena related to learned features in deep learning and transfer learning, known as Neural Collapse (NC). Within the framework of NC, we establish an error bound indicating that the misclassification error is independent of dimension when the distance between actual features and the ideal ones is smaller than a threshold. Additionally, the quality of the features in the last layer is empirically evaluated under different pre-trained models within the framework of NC, showing that a more powerful transformer leads to a better feature representation. Furthermore, we reveal that DP fine-tuning is less robust compared to fine-tuning without DP, particularly in the presence of perturbations. These observations are supported by both theoretical analyses and experimental evaluation. Moreover, to enhance the robustness of DP fine-tuning, we suggest several strategies, such as feature normalization or employing dimension reduction methods like Principal Component Analysis (PCA). Empirically, we demonstrate a significant improvement in testing accuracy by conducting PCA on the last-layer features. | [
"['Chendi Wang' 'Yuqing Zhu' 'Weijie J. Su' 'Yu-Xiang Wang']"
]
|
null | null | 2405.08921 | null | null | http://arxiv.org/pdf/2405.08921v1 | 2024-05-14T19:25:37Z | 2024-05-14T19:25:37Z | Neural Active Learning Meets the Partial Monitoring Framework | We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks. | [
"['Maxime Heuillet' 'Ola Ahmad' 'Audrey Durand']"
]
|
null | null | 2405.08944 | null | null | http://arxiv.org/pdf/2405.08944v1 | 2024-05-14T20:17:22Z | 2024-05-14T20:17:22Z | Challenges in Deploying Long-Context Transformers: A Theoretical Peak
Performance Analysis | Transformer-based long context generative models power emerging AI applications like hour-long video understanding and project-level coding agent. Deploying long context transformers (e.g., 100K to 10M tokens) is prohibitively expensive compared to short context (e.g., 4K tokens) model variants. Reducing the cost of long-context transformers is becoming a pressing research and engineering challenge starting from the year of 2024. This work describes a concurrent programming framework for quantitatively analyzing the efficiency challenges in serving multiple long-context requests under limited size of GPU high-bandwidth memory (HBM) regime. We give a detailed analysis of how all additional computational costs, compared to 4K context, trace back to textit{one single source: the large size of the KV cache}. We use a 34B GPT-3.5 level model of 50K context on A100 NVLink as a running example, and describe how its large KV cache causes four types of deployment challenges: (1) prefilling long inputs takes much longer compute time and GPU memory than short inputs; (2) after prefilling, the large KV cache residing on the GPU HBM substantially restricts the number of concurrent users being served; (3) during decoding, repeatedly reading the KV cache from HBM to SM largely increases latency; (4) when KV cache memory overflows, swapping it from HBM to DDR causes significant context switching latency. We use this framework to analyze existing works and identify possibilities of combining them to build end-to-end systems. Overall, this work offers a foundational framework for analyzing long context transformer deployment and identifies directions towards reducing the inference cost of 1M context to be as cheap as 4K. | [
"['Yao Fu']"
]
|
null | null | 2405.08958 | null | null | http://arxiv.org/pdf/2405.08958v1 | 2024-05-14T20:56:05Z | 2024-05-14T20:56:05Z | Learned radio interferometric imaging for varying visibility coverage | With the next generation of interferometric telescopes, such as the Square Kilometre Array (SKA), the need for highly computationally efficient reconstruction techniques is particularly acute. The challenge in designing learned, data-driven reconstruction techniques for radio interferometry is that they need to be agnostic to the varying visibility coverages of the telescope, since these are different for each observation. Because of this, learned post-processing or learned unrolled iterative reconstruction methods must typically be retrained for each specific observation, amounting to a large computational overhead. In this work we develop learned post-processing and unrolled iterative methods for varying visibility coverages, proposing training strategies to make these methods agnostic to variations in visibility coverage with minimal to no fine-tuning. Learned post-processing techniques are heavily dependent on the prior information encoded in training data and generalise poorly to other visibility coverages. In contrast, unrolled iterative methods, which include the telescope measurement operator inside the network, achieve state-of-the-art reconstruction quality and computation time, generalising well to other coverages and require little to no fine-tuning. Furthermore, they generalise well to realistic radio observations and are able to reconstruct the high dynamic range of these images. | [
"['Matthijs Mars' 'Marta M. Betcke' 'Jason D. McEwen']"
]
|
null | null | 2405.08961 | null | null | http://arxiv.org/pdf/2405.08961v1 | 2024-05-14T21:01:12Z | 2024-05-14T21:01:12Z | Bird's-Eye View to Street-View: A Survey | In recent years, street view imagery has grown to become one of the most important sources of geospatial data collection and urban analytics, which facilitates generating meaningful insights and assisting in decision-making. Synthesizing a street-view image from its corresponding satellite image is a challenging task due to the significant differences in appearance and viewpoint between the two domains. In this study, we screened 20 recent research papers to provide a thorough review of the state-of-the-art of how street-view images are synthesized from their corresponding satellite counterparts. The main findings are: (i) novel deep learning techniques are required for synthesizing more realistic and accurate street-view images; (ii) more datasets need to be collected for public usage; and (iii) more specific evaluation metrics need to be investigated for evaluating the generated images appropriately. We conclude that, due to applying outdated deep learning techniques, the recent literature failed to generate detailed and diverse street-view images. | [
"['Khawlah Bajbaa' 'Muhammad Usman' 'Saeed Anwar' 'Ibrahim Radwan'\n 'Abdul Bais']"
]
|
null | null | 2405.08967 | null | null | http://arxiv.org/pdf/2405.08967v2 | 2024-05-24T12:00:40Z | 2024-05-14T21:15:29Z | Gradient-Free Training of Recurrent Neural Networks using Random
Perturbations | Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle with propagating gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. Subsequently, we conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic applications | [
"['Jesus Garcia Fernandez' 'Sander Keemink' 'Marcel van Gerven']"
]
|
null | null | 2405.08969 | null | null | http://arxiv.org/pdf/2405.08969v2 | 2024-06-11T20:33:20Z | 2024-05-14T21:20:27Z | Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for
Motor-Impaired Individuals via Latent Embedding Exploitation | Hand gestures can provide a natural means of human-computer interaction and enable people who cannot speak to communicate efficiently. Existing hand gesture recognition methods heavily depend on pre-defined gestures, however, motor-impaired individuals require new gestures tailored to each individual's gesture motion and style. Gesture samples collected from different persons have distribution shifts due to their health conditions, the severity of the disability, motion patterns of the arms, etc. In this paper, we introduce the Latent Embedding Exploitation (LEE) mechanism in our replay-based Few-Shot Continual Learning (FSCL) framework that significantly improves the performance of fine-tuning a model for out-of-distribution data. Our method produces a diversified latent feature space by leveraging a preserved latent embedding known as gesture prior knowledge, along with intra-gesture divergence derived from two additional embeddings. Thus, the model can capture latent statistical structure in highly variable gestures with limited samples. We conduct an experimental evaluation using the SmartWatch Gesture and the Motion Gesture datasets. The proposed method results in an average test accuracy of 57.0%, 64.6%, and 69.3% by using one, three, and five samples for six different gestures. Our method helps motor-impaired persons leverage wearable devices, and their unique styles of movement can be learned and applied in human-computer interaction and social communication. Code is available at: https://github.com/riyadRafiq/wearable-latent-embedding-exploitation | [
"['Riyad Bin Rafiq' 'Weishi Shi' 'Mark V. Albert']"
]
|
null | null | 2405.08971 | null | null | http://arxiv.org/pdf/2405.08971v1 | 2024-05-14T21:31:11Z | 2024-05-14T21:31:11Z | Computation-Aware Kalman Filtering and Smoothing | Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with the size of the state space. This is particularly problematic in spatiotemporal regression problems, where the state dimension scales with the number of spatial observations. Existing approximate frameworks leverage low-rank approximations of the covariance matrix. Since they do not model the error introduced by the computational approximation, their predictive uncertainty estimates can be overly optimistic. In this work, we propose a probabilistic numerical method for inference in high-dimensional Gauss-Markov models which mitigates these scaling issues. Our matrix-free iterative algorithm leverages GPU acceleration and crucially enables a tunable trade-off between computational cost and predictive uncertainty. Finally, we demonstrate the scalability of our method on a large-scale climate dataset. | [
"['Marvin Pförtner' 'Jonathan Wenger' 'Jon Cockayne' 'Philipp Hennig']"
]
|
null | null | 2405.08973 | null | null | http://arxiv.org/pdf/2405.08973v1 | 2024-05-14T21:55:02Z | 2024-05-14T21:55:02Z | An adaptive approach to Bayesian Optimization with switching costs | We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where there is a trade-off between evaluating more while maintaining the same setup, or switching and restricting the number of possible evaluations due to the incurred cost. We adapt two process-constrained batch algorithms to this sequential problem formulation, and propose two new methods: one cost-aware and one cost-ignorant. We validate and compare the algorithms using a set of 7 scalable test functions in different dimensionalities and switching-cost settings for 30 total configurations. Our proposed cost-aware hyperparameter-free algorithm yields comparable results to tuned process-constrained algorithms in all settings we considered, suggesting some degree of robustness to varying landscape features and cost trade-offs. This method starts to outperform the other algorithms with increasing switching-cost. Our work broadens out from other recent Bayesian Optimization studies in resource-constrained settings that consider a batch setting only. While the contributions of this work are relevant to the general class of resource-constrained problems, they are particularly relevant to problems where adaptability to varying resource availability is of high importance | [
"['Stefan Pricopie' 'Richard Allmendinger' 'Manuel Lopez-Ibanez'\n 'Clyde Fare' 'Matt Benatan' 'Joshua Knowles']"
]
|
null | null | 2405.08975 | null | null | http://arxiv.org/pdf/2405.08975v1 | 2024-05-14T22:01:04Z | 2024-05-14T22:01:04Z | A distribution-free valid p-value for finite samples of bounded random
variables | We build a valid p-value based on a concentration inequality for bounded random variables introduced by Pelekis, Ramon and Wang. The motivation behind this work is the calibration of predictive algorithms in a distribution-free setting. The super-uniform p-value is tighter than Hoeffding and Bentkus alternatives in certain regions. Even though we are motivated by a calibration setting in a machine learning context, the ideas presented in this work are also relevant in classical statistical inference. Furthermore, we compare the power of a collection of valid p- values for bounded losses, which are presented in previous literature. | [
"['Joaquin Alvarez']"
]
|
null | null | 2405.08979 | null | null | http://arxiv.org/pdf/2405.08979v1 | 2024-05-14T22:16:52Z | 2024-05-14T22:16:52Z | drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a
Drug-Cell-Gene Heterogeneous Network | Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78% accuracy (and precision), and 76% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients. | [
"['Yoshitaka Inoue' 'Hunmin Lee' 'Tianfan Fu' 'Augustin Luna']"
]
|
null | null | 2405.08981 | null | null | http://arxiv.org/abs/2405.08981v1 | 2024-05-14T22:27:12Z | 2024-05-14T22:27:12Z | Impact of Design Decisions in Scanpath Modeling | Modeling visual saliency in graphical user interfaces (GUIs) allows to understand how people perceive GUI designs and what elements attract their attention. One aspect that is often overlooked is the fact that computational models depend on a series of design parameters that are not straightforward to decide. We systematically analyze how different design parameters affect scanpath evaluation metrics using a state-of-the-art computational model (DeepGaze++). We particularly focus on three design parameters: input image size, inhibition-of-return decay, and masking radius. We show that even small variations of these design parameters have a noticeable impact on standard evaluation metrics such as DTW or Eyenalysis. These effects also occur in other scanpath models, such as UMSS and ScanGAN, and in other datasets such as MASSVIS. Taken together, our results put forward the impact of design decisions for predicting users' viewing behavior on GUIs. | [
"['Parvin Emami' 'Yue Jiang' 'Zixin Guo' 'Luis A. Leiva']"
]
|
null | null | 2405.08989 | null | null | http://arxiv.org/pdf/2405.08989v1 | 2024-05-14T23:03:52Z | 2024-05-14T23:03:52Z | What is it for a Machine Learning Model to Have a Capability? | What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? And what sorts of evidence bear upon this question? In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. | [
"['Jacqueline Harding' 'Nathaniel Sharadin']"
]
|
null | null | 2405.08999 | null | null | http://arxiv.org/pdf/2405.08999v1 | 2024-05-14T23:47:02Z | 2024-05-14T23:47:02Z | Robust Approximate Sampling via Stochastic Gradient Barker Dynamics | Stochastic Gradient (SG) Markov Chain Monte Carlo algorithms (MCMC) are popular algorithms for Bayesian sampling in the presence of large datasets. However, they come with little theoretical guarantees and assessing their empirical performances is non-trivial. In such context, it is crucial to develop algorithms that are robust to the choice of hyperparameters and to gradients heterogeneity since, in practice, both the choice of step-size and behaviour of target gradients induce hard-to-control biases in the invariant distribution. In this work we introduce the stochastic gradient Barker dynamics (SGBD) algorithm, extending the recently developed Barker MCMC scheme, a robust alternative to Langevin-based sampling algorithms, to the stochastic gradient framework. We characterize the impact of stochastic gradients on the Barker transition mechanism and develop a bias-corrected version that, under suitable assumptions, eliminates the error due to the gradient noise in the proposal. We illustrate the performance on a number of high-dimensional examples, showing that SGBD is more robust to hyperparameter tuning and to irregular behavior of the target gradients compared to the popular stochastic gradient Langevin dynamics algorithm. | [
"['Lorenzo Mauri' 'Giacomo Zanella']"
]
|
null | null | 2405.09004 | null | null | http://arxiv.org/pdf/2405.09004v1 | 2024-05-15T00:04:08Z | 2024-05-15T00:04:08Z | Improving Sequential Market Clearing via Value-oriented Renewable Energy
Forecasting | Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. While existing deterministic market clearing fails to accommodate the uncertainty, the recently proposed stochastic market clearing struggles to achieve desirable market properties. In this work, we propose a value-oriented forecasting approach, which tactically determines the RESs generation that enters the day-ahead market. With such a forecast, the existing deterministic market clearing framework can be maintained, and the day-ahead and real-time overall operation cost is reduced. At the training phase, the forecast model parameters are estimated to minimize expected day-ahead and real-time overall operation costs, instead of minimizing forecast errors in a statistical sense. Theoretically, we derive the exact form of the loss function for training the forecast model that aligns with such a goal. For market clearing modeled by linear programs, this loss function is a piecewise linear function. Additionally, we derive the analytical gradient of the loss function with respect to the forecast, which inspires an efficient training strategy. A numerical study shows our forecasts can bring significant benefits of the overall cost reduction to deterministic market clearing, compared to quality-oriented forecasting approach. | [
"['Yufan Zhang' 'Honglin Wen' 'Yuexin Bian' 'Yuanyuan Shi']"
]
|
null | null | 2405.09005 | null | null | http://arxiv.org/pdf/2405.09005v2 | 2024-06-06T12:29:48Z | 2024-05-15T00:13:18Z | Cons-training tensor networks | In this study, we introduce a novel family of tensor networks, termed textit{constrained matrix product states} (MPS), designed to incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse block structures. These tensor networks are particularly tailored for modeling distributions with support strictly over the feasible space, offering benefits such as reducing the search space in optimization problems, alleviating overfitting, improving training efficiency, and decreasing model size. Central to our approach is the concept of a quantum region, an extension of quantum numbers traditionally used in U(1) symmetric tensor networks, adapted to capture any linear constraint, including the unconstrained scenario. We further develop a novel canonical form for these new MPS, which allow for the merging and factorization of tensor blocks according to quantum region fusion rules and permit optimal truncation schemes. Utilizing this canonical form, we apply an unsupervised training strategy to optimize arbitrary objective functions subject to discrete linear constraints. Our method's efficacy is demonstrated by solving the quadratic knapsack problem, achieving superior performance compared to a leading nonlinear integer programming solver. Additionally, we analyze the complexity and scalability of our approach, demonstrating its potential in addressing complex constrained combinatorial optimization problems. | [
"['Javier Lopez-Piqueres' 'Jing Chen']"
]
|
null | null | 2405.09014 | null | null | http://arxiv.org/pdf/2405.09014v1 | 2024-05-15T00:43:19Z | 2024-05-15T00:43:19Z | Feature-based Federated Transfer Learning: Communication Efficiency,
Robustness and Privacy | In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. Specifically, in the proposed feature-based federated learning, we design the extracted features and outputs to be uploaded instead of parameter updates. For this distributed learning model, we determine the required payload and provide comparisons with the existing schemes. Subsequently, we analyze the robustness of feature-based federated transfer learning against packet loss, data insufficiency, and quantization. Finally, we address privacy considerations by defining and analyzing label privacy leakage and feature privacy leakage, and investigating mitigating approaches. For all aforementioned analyses, we evaluate the performance of the proposed learning scheme via experiments on an image classification task and a natural language processing task to demonstrate its effectiveness. | [
"['Feng Wang' 'M. Cenk Gursoy' 'Senem Velipasalar']"
]
|
null | null | 2405.09021 | null | null | http://arxiv.org/pdf/2405.09021v1 | 2024-05-15T01:22:30Z | 2024-05-15T01:22:30Z | Deep Learning in Earthquake Engineering: A Comprehensive Review | This article surveys the growing interest in utilizing Deep Learning (DL) as a powerful tool to address challenging problems in earthquake engineering. Despite decades of advancement in domain knowledge, issues such as uncertainty in earthquake occurrence, unpredictable seismic loads, nonlinear structural responses, and community engagement remain difficult to tackle using domain-specific methods. DL offers promising solutions by leveraging its data-driven capacity for nonlinear mapping, sequential data modeling, automatic feature extraction, dimensionality reduction, optimal decision-making, etc. However, the literature lacks a comprehensive review that systematically covers a consistent scope intersecting DL and earthquake engineering. To bridge the gap, the article first discusses methodological advances to elucidate various applicable DL techniques, such as multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), autoencoder (AE), transfer learning (TL), reinforcement learning (RL), and graph neural network (GNN). A thorough research landscape is then disclosed by exploring various DL applications across different research topics, including vision-based seismic damage assessment and structural characterization, seismic demand and damage state prediction, seismic response history prediction, regional seismic risk assessment and community resilience, ground motion (GM) for engineering use, seismic response control, and the inverse problem of system/damage identification. Suitable DL techniques for each research topic are identified, emphasizing the preeminence of CNN for vision-based tasks, RNN for sequential data, RL for community resilience, and unsupervised learning for GM analysis. The article also discusses opportunities and challenges for leveraging DL in earthquake engineering research and practice. | [
"['Yazhou Xie']"
]
|
null | null | 2405.09037 | null | null | http://arxiv.org/pdf/2405.09037v1 | 2024-05-15T02:13:51Z | 2024-05-15T02:13:51Z | Unmasking Efficiency: Learning Salient Sparse Models in Non-IID
Federated Learning | In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are communicated each round between the clients and the server. We validate SSFL's effectiveness using standard non-IID benchmarks, noting marked improvements in the sparsity--accuracy trade-offs. Finally, we deploy our method in a real-world federated learning framework and report improvement in communication time. | [
"['Riyasat Ohib' 'Bishal Thapaliya' 'Gintare Karolina Dziugaite'\n 'Jingyu Liu' 'Vince Calhoun' 'Sergey Plis']"
]
|
null | null | 2405.09039 | null | null | http://arxiv.org/pdf/2405.09039v1 | 2024-05-15T02:19:34Z | 2024-05-15T02:19:34Z | SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status
Prediction | Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess unnecessary details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space. By adopting missing-aware attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. | [
"['Zhihao Yu' 'Xu Chu' 'Yujie Jin' 'Yasha Wang' 'Junfeng Zhao']"
]
|
null | null | 2405.09049 | null | null | http://arxiv.org/pdf/2405.09049v2 | 2024-05-20T10:52:46Z | 2024-05-15T02:54:11Z | Perception Without Vision for Trajectory Prediction: Ego Vehicle
Dynamics as Scene Representation for Efficient Active Learning in Autonomous
Driving | This study investigates the use of trajectory and dynamic state information for efficient data curation in autonomous driving machine learning tasks. We propose methods for clustering trajectory-states and sampling strategies in an active learning framework, aiming to reduce annotation and data costs while maintaining model performance. Our approach leverages trajectory information to guide data selection, promoting diversity in the training data. We demonstrate the effectiveness of our methods on the trajectory prediction task using the nuScenes dataset, showing consistent performance gains over random sampling across different data pool sizes, and even reaching sub-baseline displacement errors at just 50% of the data cost. Our results suggest that sampling typical data initially helps overcome the ''cold start problem,'' while introducing novelty becomes more beneficial as the training pool size increases. By integrating trajectory-state-informed active learning, we demonstrate that more efficient and robust autonomous driving systems are possible and practical using low-cost data curation strategies. | [
"['Ross Greer' 'Mohan Trivedi']"
]
|
null | null | 2405.09052 | null | null | http://arxiv.org/pdf/2405.09052v1 | 2024-05-15T02:58:19Z | 2024-05-15T02:58:19Z | Dielectric Tensor Prediction for Inorganic Materials Using Latent
Information from Preferred Potential | Dielectrics are materials with widespread applications in flash memory, central processing units, photovoltaics, capacitors, etc. However, the availability of public dielectric data remains limited, hindering research and development efforts. Previously, machine learning models focused on predicting dielectric constants as scalars, overlooking the importance of dielectric tensors in understanding material properties under directional electric fields for material design and simulation. This study demonstrates the value of common equivariant structural embedding features derived from a universal neural network potential in enhancing the prediction of dielectric properties. To integrate channel information from various-rank latent features while preserving the desired SE(3) equivariance to the second-rank dielectric tensors, we design an equivariant readout decoder to predict the total, electronic, and ionic dielectric tensors individually, and compare our model with the state-of-the-art models. Finally, we evaluate our model by conducting virtual screening on thermodynamical stable structure candidates in Materials Project. The material Batextsubscript{2}SmTaOtextsubscript{6} with large band gaps ($E_g=3.36 mathrm{eV}$) and dielectric constants ($epsilon=93.81$) is successfully identified out of the 14k candidate set. The results show that our methods give good accuracy on predicting dielectric tensors of inorganic materials, emphasizing their potential in contributing to the discovery of novel dielectrics. | [
"['Zetian Mao' 'Wenwen Li' 'Jethro Tan']"
]
|
null | null | 2405.09057 | null | null | http://arxiv.org/pdf/2405.09057v1 | 2024-05-15T03:08:21Z | 2024-05-15T03:08:21Z | Response Matching for generating materials and molecules | Machine learning has recently emerged as a powerful tool for generating new molecular and material structures. The success of state-of-the-art models stems from their ability to incorporate physical symmetries, such as translation, rotation, and periodicity. Here, we present a novel generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Consequently, any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching to such response is closely related to score matching in diffusion models. By employing the combination of a machine learning interatomic potential and random structure search as the denoising model, RM exploits the locality of atomic interactions, and inherently respects permutation, translation, rotation, and periodic invariances. RM is the first model to handle both molecules and bulk materials under the same framework. We demonstrate the efficiency and generalization of RM across three systems: a small organic molecular dataset, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration. | [
"['Bingqing Cheng']"
]
|
null | null | 2405.09061 | null | null | http://arxiv.org/pdf/2405.09061v2 | 2024-05-16T06:26:43Z | 2024-05-15T03:17:30Z | Improving Transformers using Faithful Positional Encoding | We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on solid mathematical grounds and has a guarantee of not losing information about the positional order of the input sequence. We show that the new encoding approach systematically improves the prediction performance in the time-series classification task. | [
"['Tsuyoshi Idé' 'Jokin Labaien' 'Pin-Yu Chen']"
]
|
null | null | 2405.09062 | null | null | http://arxiv.org/pdf/2405.09062v4 | 2024-07-03T17:33:58Z | 2024-05-15T03:26:01Z | Naturalistic Music Decoding from EEG Data via Latent Diffusion Models | In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic pieces, the focus here is on intricate music featuring a diverse array of instruments, voices, and effects, rich in harmonics and timbre. This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data, employing an end-to-end training approach directly on raw data without the need for manual pre-processing and channel selection. We train our models on the public NMED-T dataset and perform quantitative evaluation proposing neural embedding-based metrics. We additionally perform song classification based on the generated tracks. Our work contributes to the ongoing research in neural decoding and brain-computer interfaces, offering insights into the feasibility of using EEG data for complex auditory information reconstruction. | [
"['Emilian Postolache' 'Natalia Polouliakh' 'Hiroaki Kitano'\n 'Akima Connelly' 'Emanuele Rodolà' 'Luca Cosmo' 'Taketo Akama']"
]
|
null | null | 2405.09076 | null | null | http://arxiv.org/pdf/2405.09076v1 | 2024-05-15T04:01:47Z | 2024-05-15T04:01:47Z | Enhancing Airline Customer Satisfaction: A Machine Learning and Causal
Analysis Approach | This study explores the enhancement of customer satisfaction in the airline industry, a critical factor for retaining customers and building brand reputation, which are vital for revenue growth. Utilizing a combination of machine learning and causal inference methods, we examine the specific impact of service improvements on customer satisfaction, with a focus on the online boarding pass experience. Through detailed data analysis involving several predictive and causal models, we demonstrate that improvements in the digital aspects of customer service significantly elevate overall customer satisfaction. This paper highlights how airlines can strategically leverage these insights to make data-driven decisions that enhance customer experiences and, consequently, their market competitiveness. | [
"['Tejas Mirthipati']"
]
|
null | null | 2405.09086 | null | null | http://arxiv.org/pdf/2405.09086v1 | 2024-05-15T04:47:31Z | 2024-05-15T04:47:31Z | Chaos-based reinforcement learning with TD3 | Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. This approach offers a model for considering how the biological brain can create variability in its behavior and learn in an exploratory manner. At the same time, it is a learning model that has the ability to automatically switch between exploration and exploitation modes and the potential to realize higher explorations that reflect what it has learned so far. However, the learning algorithms in CBRL have not been well-established in previous studies and have yet to incorporate recent advances in reinforcement learning. This study introduced Twin Delayed Deep Deterministic Policy Gradients (TD3), which is one of the state-of-the-art deep reinforcement learning algorithms that can treat deterministic and continuous action spaces, to CBRL. The validation results provide several insights. First, TD3 works as a learning algorithm for CBRL in a simple goal-reaching task. Second, CBRL agents with TD3 can autonomously suppress their exploratory behavior as learning progresses and resume exploration when the environment changes. Finally, examining the effect of the agent's chaoticity on learning shows that extremely strong chaos negatively impacts the flexible switching between exploration and exploitation. | [
"['Toshitaka Matsuki' 'Yusuke Sakemi' 'Kazuyuki Aihara']"
]
|
null | null | 2405.09096 | null | null | http://arxiv.org/pdf/2405.09096v2 | 2024-05-20T18:32:03Z | 2024-05-15T05:13:20Z | Optimizing Sensor Network Design for Multiple Coverage | Sensor placement optimization methods have been studied extensively. They can be applied to a wide range of applications, including surveillance of known environments, optimal locations for 5G towers, and placement of missile defense systems. However, few works explore the robustness and efficiency of the resulting sensor network concerning sensor failure or adversarial attacks. This paper addresses this issue by optimizing for the least number of sensors to achieve multiple coverage of non-simply connected domains by a prescribed number of sensors. We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks and derive theoretical bounds on the network's optimality. We further introduce a Deep Learning model to accelerate the algorithm for near real-time computations. The Deep Learning model requires the generation of training examples. Correspondingly, we show that understanding the geometric properties of the training data set provides important insights into the performance and training process of deep learning techniques. Finally, we demonstrate that a simple parallel version of the greedy approach using a simpler objective can be highly competitive. | [
"['Lukas Taus' 'Yen-Hsi Richard Tsai']"
]
|
null | null | 2405.09106 | null | null | http://arxiv.org/pdf/2405.09106v1 | 2024-05-15T05:43:16Z | 2024-05-15T05:43:16Z | Minimisation of Polyak-Łojasewicz Functions Using Random Zeroth-Order
Oracles | The application of a zeroth-order scheme for minimising Polyak-L{}ojasewicz (PL) functions is considered. The framework is based on exploiting a random oracle to estimate the function gradient. The convergence of the algorithm to a global minimum in the unconstrained case and to a neighbourhood of the global minimum in the constrained case along with their corresponding complexity bounds are presented. The theoretical results are demonstrated via numerical examples. | [
"['Amir Ali Farzin' 'Iman Shames']"
]
|
null | null | 2405.09109 | null | null | http://arxiv.org/abs/2405.09109v2 | 2024-05-18T17:47:42Z | 2024-05-15T05:51:41Z | Motion Prediction with Gaussian Processes for Safe Human-Robot
Interaction in Virtual Environments | Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, thus increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts for virtual reality applications, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection based on hand motion and gaze to improve the time for the robot and human security in a virtual environment. We then studied the effect of prediction. Results from comparisons show that the prediction models improved the robot time by 3% and safety by 17%. When used alongside gaze, prediction with Gaussian process models resulted in an improvement of the robot time by 2% and the safety by 13%. | [
"['Stanley Mugisha' 'Vamsi Krishna Guda' 'Christine Chevallereau'\n 'Damien Chablat' 'Matteo Zoppi']"
]
|
null | null | 2405.09113 | null | null | http://arxiv.org/pdf/2405.09113v1 | 2024-05-15T06:11:24Z | 2024-05-15T06:11:24Z | Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained
Optimization | Recent research indicates that large language models (LLMs) are susceptible to jailbreaking attacks that can generate harmful content. This paper introduces a novel token-level attack method, Adaptive Dense-to-Sparse Constrained Optimization (ADC), which effectively jailbreaks several open-source LLMs. Our approach relaxes the discrete jailbreak optimization into a continuous optimization and progressively increases the sparsity of the optimizing vectors. Consequently, our method effectively bridges the gap between discrete and continuous space optimization. Experimental results demonstrate that our method is more effective and efficient than existing token-level methods. On Harmbench, our method achieves state of the art attack success rate on seven out of eight LLMs. Code will be made available. Trigger Warning: This paper contains model behavior that can be offensive in nature. | [
"['Kai Hu' 'Weichen Yu' 'Tianjun Yao' 'Xiang Li' 'Wenhe Liu' 'Lijun Yu'\n 'Yining Li' 'Kai Chen' 'Zhiqiang Shen' 'Matt Fredrikson']"
]
|
null | null | 2405.09118 | null | null | http://arxiv.org/pdf/2405.09118v2 | 2024-07-04T20:49:51Z | 2024-05-15T06:23:59Z | BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic
Platform | In this article, we focus on the critical tasks of plant protection in arable farms, addressing a modern challenge in agriculture: integrating ecological considerations into the operational strategy of precision weeding robots like bbot. This article presents the recent advancements in weed management algorithms and the real-world performance of bbot at the University of Bonn's Klein-Altendorf campus. We present a novel Rolling-view observation model for the BonnBot-Is weed monitoring section which leads to an average absolute weeding performance enhancement of $3.4%$. Furthermore, for the first time, we show how precision weeding robots could consider bio-diversity-aware concerns in challenging weeding scenarios. We carried out comprehensive weeding experiments in sugar-beet fields, covering both weed-only and mixed crop-weed situations, and introduced a new dataset compatible with precision weeding. Our real-field experiments revealed that our weeding approach is capable of handling diverse weed distributions, with a minimal loss of only $11.66%$ attributable to intervention planning and $14.7%$ to vision system limitations highlighting required improvements of the vision system. | [
"['Alireza Ahmadi' 'Michael Halstead' 'Claus Smitt' 'Chris McCool']"
]
|
null | null | 2405.09133 | null | null | http://arxiv.org/pdf/2405.09133v1 | 2024-05-15T06:57:18Z | 2024-05-15T06:57:18Z | Overcoming Domain Drift in Online Continual Learning | Online Continual Learning (OCL) empowers machine learning models to acquire new knowledge online across a sequence of tasks. However, OCL faces a significant challenge: catastrophic forgetting, wherein the model learned in previous tasks is substantially overwritten upon encountering new tasks, leading to a biased forgetting of prior knowledge. Moreover, the continual doman drift in sequential learning tasks may entail the gradual displacement of the decision boundaries in the learned feature space, rendering the learned knowledge susceptible to forgetting. To address the above problem, in this paper, we propose a novel rehearsal strategy, termed Drift-Reducing Rehearsal (DRR), to anchor the domain of old tasks and reduce the negative transfer effects. First, we propose to select memory for more representative samples guided by constructed centroids in a data stream. Then, to keep the model from domain chaos in drifting, a two-level angular cross-task Contrastive Margin Loss (CML) is proposed, to encourage the intra-class and intra-task compactness, and increase the inter-class and inter-task discrepancy. Finally, to further suppress the continual domain drift, we present an optional Centorid Distillation Loss (CDL) on the rehearsal memory to anchor the knowledge in feature space for each previous old task. Extensive experimental results on four benchmark datasets validate that the proposed DRR can effectively mitigate the continual domain drift and achieve the state-of-the-art (SOTA) performance in OCL. | [
"['Fan Lyu' 'Daofeng Liu' 'Linglan Zhao' 'Zhang Zhang' 'Fanhua Shang'\n 'Fuyuan Hu' 'Wei Feng' 'Liang Wang']"
]
|
null | null | 2405.09153 | null | null | http://arxiv.org/pdf/2405.09153v1 | 2024-05-15T07:32:43Z | 2024-05-15T07:32:43Z | Adapting Abstract Meaning Representation Parsing to the Clinical
Narrative -- the SPRING THYME parser | This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88% on the THYME corpus's colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser's robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations. | [
"['Jon Z. Cai' 'Kristin Wright-Bettner' 'Martha Palmer'\n 'Guergana K. Savova' 'James H. Martin']"
]
|
null | null | 2405.09176 | null | null | http://arxiv.org/pdf/2405.09176v1 | 2024-05-15T08:33:41Z | 2024-05-15T08:33:41Z | Cross-Input Certified Training for Universal Perturbations | Existing work in trustworthy machine learning primarily focuses on single-input adversarial perturbations. In many real-world attack scenarios, input-agnostic adversarial attacks, e.g. universal adversarial perturbations (UAPs), are much more feasible. Current certified training methods train models robust to single-input perturbations but achieve suboptimal clean and UAP accuracy, thereby limiting their applicability in practical applications. We propose a novel method, CITRUS, for certified training of networks robust against UAP attackers. We show in an extensive evaluation across different datasets, architectures, and perturbation magnitudes that our method outperforms traditional certified training methods on standard accuracy (up to 10.3%) and achieves SOTA performance on the more practical certified UAP accuracy metric. | [
"['Changming Xu' 'Gagandeep Singh']"
]
|
null | null | 2405.09204 | null | null | http://arxiv.org/pdf/2405.09204v1 | 2024-05-15T09:23:21Z | 2024-05-15T09:23:21Z | Lens functions for exploring UMAP Projections with Domain Knowledge | Dimensionality reduction algorithms are often used to visualise high-dimensional data. Previously, studies have used prior information to enhance or suppress expected patterns in projections. In this paper, we adapt such techniques for domain knowledge guided interactive exploration. Inspired by Mapper and STAD, we present three types of lens functions for UMAP, a state-of-the-art dimensionality reduction algorithm. Lens functions enable analysts to adapt projections to their questions, revealing otherwise hidden patterns. They filter the modelled connectivity to explore the interaction between manually selected features and the data's structure, creating configurable perspectives each potentially revealing new insights. The effectiveness of the lens functions is demonstrated in two use cases and their computational cost is analysed in a synthetic benchmark. Our implementation is available in an open-source Python package: https://github.com/vda-lab/lensed_umap. | [
"['Daniel M. Bot' 'Jan Aerts']"
]
|
null | null | 2405.09212 | null | null | http://arxiv.org/pdf/2405.09212v1 | 2024-05-15T09:38:52Z | 2024-05-15T09:38:52Z | SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe
Trajectory Planning Problems in Robotics | Model Predictive Control (MPC)-based trajectory planning has been widely used in robotics, and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this paper, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality. | [
"['Yifan Liu' 'You Wang' 'Guang Li']"
]
|
null | null | 2405.09220 | null | null | http://arxiv.org/pdf/2405.09220v2 | 2024-05-27T05:25:05Z | 2024-05-15T09:59:37Z | ALPINE: Unveiling the Planning Capability of Autoregressive Learning in
Language Models | In this paper, we present the findings of our Project ALPINE which stands for ``Autoregressive Learning for Planning In NEtworks." Project ALPINE initiates a theoretical investigation into the development of planning capabilities in Transformer-based language models through their autoregressive learning mechanisms, aiming to identify any potential limitations in their planning abilities. We abstract planning as a network path-finding task where the objective is to generate a valid path from a specified source node to a designated target node. In terms of expressiveness, we show that the Transformer is capable of executing path-finding by embedding the adjacency and reachability matrices within its weights. Our theoretical analysis of the gradient-based learning dynamic of the Transformer reveals that the Transformer is capable of learning both the adjacency matrix and a limited form of the reachability matrix. These theoretical insights are then validated through experiments, which demonstrate that the Transformer indeed learns the adjacency matrix and an incomplete reachability matrix, which aligns with the predictions made in our theoretical analysis. Additionally, when applying our methodology to a real-world planning benchmark, called Blocksworld, our observations remain consistent. Our theoretical and empirical analyses further unveil a potential limitation of Transformer in path-finding: it cannot identify reachability relationships through transitivity, and thus would fail when path concatenation is needed to generate a path. In summary, our findings shed new light on how the internal mechanisms of autoregressive learning enable planning in networks. This study may contribute to our understanding of the general planning capabilities in other related domains. | [
"['Siwei Wang' 'Yifei Shen' 'Shi Feng' 'Haoran Sun' 'Shang-Hua Teng'\n 'Wei Chen']"
]
|
null | null | 2405.09221 | null | null | http://arxiv.org/pdf/2405.09221v1 | 2024-05-15T10:02:47Z | 2024-05-15T10:02:47Z | Bridging the gap in online hate speech detection: a comparative analysis
of BERT and traditional models for homophobic content identification on
X/Twitter | Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis. | [
"['Josh McGiff' 'Nikola S. Nikolov']"
]
|
null | null | 2405.09224 | null | null | http://arxiv.org/pdf/2405.09224v1 | 2024-05-15T10:04:44Z | 2024-05-15T10:04:44Z | Perception-Inspired Graph Convolution for Music Understanding Tasks | We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensions of music, pitch and rhythm, and considers both relative and absolute representations of these components. We evaluate our approach on four different musical understanding problems: monophonic voice separation, harmonic analysis, cadence detection, and composer identification which, in abstract terms, translate to different graph learning problems, namely, node classification, link prediction, and graph classification. Our experiments demonstrate that MusGConv improves the performance on three of the aforementioned tasks while being conceptually very simple and efficient. We interpret this as evidence that it is beneficial to include perception-informed processing of fundamental musical concepts when developing graph network applications on musical score data. | [
"['Emmanouil Karystinaios' 'Francesco Foscarin' 'Gerhard Widmer']"
]
|
null | null | 2405.09244 | null | null | http://arxiv.org/abs/2405.09244v1 | 2024-05-15T10:55:16Z | 2024-05-15T10:55:16Z | NeuralCMS: A deep learning approach to study Jupiter's interior | NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary properties, such as the cloud-level temperature, composition, and core features, requiring the computation of ~10^9 interior models. We propose an efficient deep neural network (DNN) model to generate high-precision wide-ranged interior models based on the very accurate but computationally demanding concentric MacLaurin spheroid (CMS) method. We trained a sharing-based DNN with a large set of CMS results for a four-layer interior model of Jupiter, including a dilute core, to accurately predict the gravity moments and mass, given a combination of interior features. We evaluated the performance of the trained DNN (NeuralCMS) to inspect its predictive limitations. NeuralCMS shows very good performance in predicting the gravity moments, with errors comparable with the uncertainty due to differential rotation, and a very accurate mass prediction. This allowed us to perform a broad parameter space search by computing only ~10^4 actual CMS interior models, resulting in a large sample of plausible interior structures, and reducing the computation time by a factor of 10^5. Moreover, we used a DNN explainability algorithm to analyze the impact of the parameters setting the interior model on the predicted observables, providing information on their nonlinear relation. | [
"['Maayan Ziv' 'Eli Galanti' 'Amir Sheffer' 'Saburo Howard'\n 'Tristan Guillot' 'Yohai Kaspi']"
]
|
null | null | 2405.09247 | null | null | http://arxiv.org/pdf/2405.09247v1 | 2024-05-15T11:00:42Z | 2024-05-15T11:00:42Z | Graph Neural Network based Handwritten Trajectories Recognition | The graph neural networks has been proved to be an efficient machine learning technique in real life applications. The handwritten recognition is one of the useful area in real life use where both offline and online handwriting recognition are required. The chain code as feature extraction technique has shown significant results in literature and we have been able to use chain codes with graph neural networks. To the best of our knowledge, this work presents first time a novel combination of handwritten trajectories features as chain codes and graph neural networks together. The handwritten trajectories for offline handwritten text has been evaluated using recovery of drawing order, whereas online handwritten trajectories are directly used with chain codes. Our results prove that present combination surpass previous results and minimize error rate in few epochs only. | [
"['Anuj Sharma' 'Sukhdeep Singh' 'S Ratna']"
]
|
null | null | 2405.09251 | null | null | http://arxiv.org/pdf/2405.09251v1 | 2024-05-15T11:07:40Z | 2024-05-15T11:07:40Z | Does Machine Bring in Extra Bias in Learning? Approximating Fairness in
Models Promptly | Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly in high-stakes domains. Existing techniques for assessing the discrimination level of ML models include commonly used group and individual fairness measures. However, these two types of fairness measures are usually hard to be compatible with each other, and even two different group fairness measures might be incompatible as well. To address this issue, we investigate to evaluate the discrimination level of classifiers from a manifold perspective and propose a "harmonic fairness measure via manifolds (HFM)" based on distances between sets. Yet the direct calculation of distances might be too expensive to afford, reducing its practical applicability. Therefore, we devise an approximation algorithm named "Approximation of distance between sets (ApproxDist)" to facilitate accurate estimation of distances, and we further demonstrate its algorithmic effectiveness under certain reasonable assumptions. Empirical results indicate that the proposed fairness measure HFM is valid and that the proposed ApproxDist is effective and efficient. | [
"['Yijun Bian' 'Yujie Luo']"
]
|
null | null | 2405.09273 | null | null | http://arxiv.org/pdf/2405.09273v2 | 2024-05-22T06:08:03Z | 2024-05-15T11:42:41Z | Fair Generalized Linear Mixed Models | When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. E.g., predictions from fair machine learning models should not discriminate against sensitive variables such as sexual orientation and ethnicity. The training data often in obtained from social surveys. In social surveys, oftentimes the data collection process is a strata sampling, e.g. due to cost restrictions. In strata samples, the assumption of independence between the observation is not fulfilled. Hence, if the machine learning models do not account for the strata correlations, the results may be biased. Especially high is the bias in cases where the strata assignment is correlated to the variable of interest. We present in this paper an algorithm that can handle both problems simultaneously, and we demonstrate the impact of stratified sampling on the quality of fair machine learning predictions in a reproducible simulation study. | [
"['Jan Pablo Burgard' 'João Vitor Pamplona']"
]
|
null | null | 2405.09274 | null | null | http://arxiv.org/pdf/2405.09274v1 | 2024-05-15T11:42:42Z | 2024-05-15T11:42:42Z | Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study | In this work, we systematically investigate the efficacy of dynamic activation mechanisms within the LLaMA family of language models. Despite the potential of dynamic activation methods to reduce computation and increase speed in models using the ReLU activation function, our empirical findings have uncovered several inherent pitfalls in the current dynamic activation schemes. Through extensive experiments across various dynamic activation strategies, we demonstrate that LLaMA models usually underperform when compared to their ReLU counterparts, particularly in scenarios demanding high sparsity ratio. We attribute these deficiencies to a combination of factors: 1) the inherent complexity of dynamically predicting activation heads and neurons; 2) the inadequate sparsity resulting from activation functions; 3) the insufficient preservation of information resulting from KV cache skipping. Our analysis not only sheds light on the limitations of dynamic activation in the context of large-scale LLaMA models but also proposes roadmaps for enhancing the design of future sparsity schemes. | [
"['Chi Ma' 'Mincong Huang' 'Chao Wang' 'Yujie Wang' 'Lei Yu']"
]
|
null | null | 2405.09276 | null | null | http://arxiv.org/pdf/2405.09276v1 | 2024-05-15T11:46:47Z | 2024-05-15T11:46:47Z | Dual-Segment Clustering Strategy for Federated Learning in Heterogeneous
Environments | Federated learning (FL) is a distributed machine learning paradigm with high efficiency and low communication load, only transmitting parameters or gradients of network. However, the non-independent and identically distributed (Non-IID) data characteristic has a negative impact on this paradigm. Furthermore, the heterogeneity of communication quality will significantly affect the accuracy of parameter transmission, causing a degradation in the performance of the FL system or even preventing its convergence. This letter proposes a dual-segment clustering (DSC) strategy, which first clusters the clients according to the heterogeneous communication conditions and then performs a second clustering by the sample size and label distribution, so as to solve the problem of data and communication heterogeneity. Experimental results show that the DSC strategy proposed in this letter can improve the convergence rate of FL, and has superiority on accuracy in a heterogeneous environment compared with the classical algorithm of cluster. | [
"['Pengcheng Sun' 'Erwu Liu' 'Wei Ni' 'Kanglei Yu' 'Rui Wang'\n 'Abbas Jamalipour']"
]
|
null | null | 2405.09285 | null | null | http://arxiv.org/pdf/2405.09285v1 | 2024-05-15T12:09:24Z | 2024-05-15T12:09:24Z | Positional Knowledge is All You Need: Position-induced Transformer (PiT)
for Operator Learning | Operator learning for Partial Differential Equations (PDEs) is rapidly emerging as a promising approach for surrogate modeling of intricate systems. Transformers with the self-attention mechanism$unicode{x2013}$a powerful tool originally designed for natural language processing$unicode{x2013}$have recently been adapted for operator learning. However, they confront challenges, including high computational demands and limited interpretability. This raises a critical question: Is there a more efficient attention mechanism for Transformer-based operator learning? This paper proposes the Position-induced Transformer (PiT), built on an innovative position-attention mechanism, which demonstrates significant advantages over the classical self-attention in operator learning. Position-attention draws inspiration from numerical methods for PDEs. Different from self-attention, position-attention is induced by only the spatial interrelations of sampling positions for input functions of the operators, and does not rely on the input function values themselves, thereby greatly boosting efficiency. PiT exhibits superior performance over current state-of-the-art neural operators in a variety of complex operator learning tasks across diverse PDE benchmarks. Additionally, PiT possesses an enhanced discretization convergence feature, compared to the widely-used Fourier neural operator. | [
"['Junfeng Chen' 'Kailiang Wu']"
]
|
null | null | 2405.09296 | null | null | http://arxiv.org/pdf/2405.09296v1 | 2024-05-15T12:37:03Z | 2024-05-15T12:37:03Z | Tight Bounds for Online Convex Optimization with Adversarial Constraints | A well-studied generalization of the standard online convex optimization (OCO) is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner after the action for that round is chosen. The objective is to design an online policy that simultaneously achieves a small regret while ensuring small cumulative constraint violation (CCV) against an adaptive adversary. A long-standing open question in COCO is whether an online policy can simultaneously achieve $O(sqrt{T})$ regret and $O(sqrt{T})$ CCV without any restrictive assumptions. For the first time, we answer this in the affirmative and show that an online policy can simultaneously achieve $O(sqrt{T})$ regret and $tilde{O}(sqrt{T})$ CCV. We establish this result by effectively combining the adaptive regret bound of the AdaGrad algorithm with Lyapunov optimization - a classic tool from control theory. Surprisingly, the analysis is short and elegant. | [
"['Abhishek Sinha' 'Rahul Vaze']"
]
|
null | null | 2405.09305 | null | null | http://arxiv.org/pdf/2405.09305v1 | 2024-05-15T12:49:57Z | 2024-05-15T12:49:57Z | Gradient Boosted Filters For Signal Processing | Gradient boosted decision trees have achieved remarkable success in several domains, particularly those that work with static tabular data. However, the application of gradient boosted models to signal processing is underexplored. In this work, we introduce gradient boosted filters for dynamic data, by employing Hammerstein systems in place of decision trees. We discuss the relationship of our approach to the Volterra series, providing the theoretical underpinning for its application. We demonstrate the effective generalizability of our approach with examples. | [
"['Jose A. Lopez' 'Georg Stemmer' 'Hector A. Cordourier']"
]
|
null | null | 2405.09308 | null | null | http://arxiv.org/pdf/2405.09308v1 | 2024-05-15T13:03:41Z | 2024-05-15T13:03:41Z | TimeX++: Learning Time-Series Explanations with Information Bottleneck | Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions and distributional shift issues. To address these issues, we introduce a simple yet practical objective function for time series explainable learning. The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues. We further present TimeX++, a novel explanation framework that leverages a parametric network to produce explanation-embedded instances that are both in-distributed and label-preserving. We evaluate TimeX++ on both synthetic and real-world datasets comparing its performance against leading baselines, and validate its practical efficacy through case studies in a real-world environmental application. Quantitative and qualitative evaluations show that TimeX++ outperforms baselines across all datasets, demonstrating a substantial improvement in explanation quality for time series data. The source code is available at url{https://github.com/zichuan-liu/TimeXplusplus}. | [
"['Zichuan Liu' 'Tianchun Wang' 'Jimeng Shi' 'Xu Zheng' 'Zhuomin Chen'\n 'Lei Song' 'Wenqian Dong' 'Jayantha Obeysekera' 'Farhad Shirani'\n 'Dongsheng Luo']"
]
|
null | null | 2405.09312 | null | null | http://arxiv.org/pdf/2405.09312v3 | 2024-07-09T19:20:57Z | 2024-05-15T13:11:28Z | Agnostic Active Learning of Single Index Models with Linear Sample
Complexity | We study active learning methods for single index models of the form $F({mathbf x}) = f(langle {mathbf w}, {mathbf x}rangle)$, where $f:mathbb{R} to mathbb{R}$ and ${mathbf x,mathbf w} in mathbb{R}^d$. In addition to their theoretical interest as simple examples of non-linear neural networks, single index models have received significant recent attention due to applications in scientific machine learning like surrogate modeling for partial differential equations (PDEs). Such applications require sample-efficient active learning methods that are robust to adversarial noise. I.e., that work even in the challenging agnostic learning setting. We provide two main results on agnostic active learning of single index models. First, when $f$ is known and Lipschitz, we show that $tilde{O}(d)$ samples collected via {statistical leverage score sampling} are sufficient to learn a near-optimal single index model. Leverage score sampling is simple to implement, efficient, and already widely used for actively learning linear models. Our result requires no assumptions on the data distribution, is optimal up to log factors, and improves quadratically on a recent ${O}(d^{2})$ bound of cite{gajjar2023active}. Second, we show that $tilde{O}(d)$ samples suffice even in the more difficult setting when $f$ is emph{unknown}. Our results leverage tools from high dimensional probability, including Dudley's inequality and dual Sudakov minoration, as well as a novel, distribution-aware discretization of the class of Lipschitz functions. | [
"['Aarshvi Gajjar' 'Wai Ming Tai' 'Xingyu Xu' 'Chinmay Hegde' 'Yi Li'\n 'Christopher Musco']"
]
|
null | null | 2405.09318 | null | null | http://arxiv.org/pdf/2405.09318v1 | 2024-05-15T13:19:43Z | 2024-05-15T13:19:43Z | Transfer Learning in Pre-Trained Large Language Models for Malware
Detection Based on System Calls | In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware exploits vulnerabilities through stealth methods, often evading traditional detection mechanisms such as software signatures. The application of ML/DL in vulnerability detection has been extensively explored in the literature. However, current ML/DL vulnerability detection methods struggle with understanding the context and intent behind complex attacks. Integrating large language models (LLMs) with system call analysis offers a promising approach to enhance malware detection. This work presents a novel framework leveraging LLMs to classify malware based on system call data. The framework uses transfer learning to adapt pre-trained LLMs for malware detection. By retraining LLMs on a dataset of benign and malicious system calls, the models are refined to detect signs of malware activity. Experiments with a dataset of over 1TB of system calls demonstrate that models with larger context sizes, such as BigBird and Longformer, achieve superior accuracy and F1-Score of approximately 0.86. The results highlight the importance of context size in improving detection rates and underscore the trade-offs between computational complexity and performance. This approach shows significant potential for real-time detection in high-stakes environments, offering a robust solution to evolving cyber threats. | [
"['Pedro Miguel Sánchez Sánchez' 'Alberto Huertas Celdrán' 'Gérôme Bovet'\n 'Gregorio Martínez Pérez']"
]
|
null | null | 2405.09321 | null | null | http://arxiv.org/pdf/2405.09321v1 | 2024-05-15T13:22:39Z | 2024-05-15T13:22:39Z | ReconBoost: Boosting Can Achieve Modality Reconcilement | This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://github.com/huacong/ReconBoost. | [
"['Cong Hua' 'Qianqian Xu' 'Shilong Bao' 'Zhiyong Yang' 'Qingming Huang']"
]
|
null | null | 2405.09324 | null | null | http://arxiv.org/pdf/2405.09324v1 | 2024-05-15T13:25:34Z | 2024-05-15T13:25:34Z | Learning Coarse-Grained Dynamics on Graph | We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs. Our main idea is to systematically determine the GNN architecture by inspecting how the leading term of the Mori-Zwanzig memory term depends on the coarse-grained interaction coefficients that encode the graph topology. Based on this analysis, we found that the appropriate GNN architecture that will account for $K$-hop dynamical interactions has to employ a Message Passing (MP) mechanism with at least $2K$ steps. We also deduce that the memory length required for an accurate closure model decreases as a function of the interaction strength under the assumption that the interaction strength exhibits a power law that decays as a function of the hop distance. Supporting numerical demonstrations on two examples, a heterogeneous Kuramoto oscillator model and a power system, suggest that the proposed GNN architecture can predict the coarse-grained dynamics under fixed and time-varying graph topologies. | [
"['Yin Yu' 'John Harlim' 'Daning Huang' 'Yan Li']"
]
|
null | null | 2405.09360 | null | null | http://arxiv.org/pdf/2405.09360v2 | 2024-06-17T18:38:17Z | 2024-05-15T14:13:35Z | The Unfairness of $\varepsilon$-Fairness | Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics may not fully capture the real-world consequences of unfairness. In this article, we adopt a utility-based approach to more accurately measure the real-world impacts of decision-making process. In particular, we show that if the concept of $varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. Additionally, we address the common issue of unavailable data on false negatives by proposing a reduced setting that still captures essential fairness considerations. We illustrate our findings with two real-world examples: college admissions and credit risk assessment. Our analysis reveals that while traditional probability-based evaluations might suggest fairness, a utility-based approach uncovers the necessary actions to truly achieve equality. For instance, in the college admission case, we find that enhancing completion rates is crucial for ensuring fairness. Summarizing, this paper highlights the importance of considering the real-world context when evaluating fairness. | [
"['Tolulope Fadina' 'Thorsten Schmidt']"
]
|
null | null | 2405.09362 | null | null | http://arxiv.org/pdf/2405.09362v2 | 2024-05-28T11:07:32Z | 2024-05-15T14:15:09Z | On the Saturation Effect of Kernel Ridge Regression | The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this paper, we provide a proof of this long-standing conjecture. | [
"['Yicheng Li' 'Haobo Zhang' 'Qian Lin']"
]
|
null | null | 2405.09394 | null | null | http://arxiv.org/pdf/2405.09394v1 | 2024-05-15T14:50:46Z | 2024-05-15T14:50:46Z | SA-FedLora: Adaptive Parameter Allocation for Efficient Federated
Learning with LoRA Tuning | Fine-tuning large-scale pre-trained models via transfer learning is an emerging important paradigm for a wide range of downstream tasks, with performance heavily reliant on extensive data. Federated learning (FL), as a distributed framework, provides a secure solution to train models on local datasets while safeguarding raw sensitive data. However, FL networks encounter high communication costs due to the massive parameters of large-scale pre-trained models, necessitating parameter-efficient methods. Notably, parameter efficient fine tuning, such as Low-Rank Adaptation (LoRA), has shown remarkable success in fine-tuning pre-trained models. However, prior research indicates that the fixed parameter budget may be prone to the overfitting or slower convergence. To address this challenge, we propose a Simulated Annealing-based Federated Learning with LoRA tuning (SA-FedLoRA) approach by reducing trainable parameters. Specifically, SA-FedLoRA comprises two stages: initiating and annealing. (1) In the initiating stage, we implement a parameter regularization approach during the early rounds of aggregation, aiming to mitigate client drift and accelerate the convergence for the subsequent tuning. (2) In the annealing stage, we allocate higher parameter budget during the early 'heating' phase and then gradually shrink the budget until the 'cooling' phase. This strategy not only facilitates convergence to the global optimum but also reduces communication costs. Experimental results demonstrate that SA-FedLoRA is an efficient FL, achieving superior performance to FedAvg and significantly reducing communication parameters by up to 93.62%. | [
"['Yuning Yang' 'Xiaohong Liu' 'Tianrun Gao' 'Xiaodong Xu' 'Guangyu Wang']"
]
|
null | null | 2405.09412 | null | null | http://arxiv.org/pdf/2405.09412v1 | 2024-05-15T15:07:31Z | 2024-05-15T15:07:31Z | Distinguishing Tor From Other Encrypted Network Traffic Through
Character Analysis | For journalists reporting from a totalitarian regime, whistleblowers and resistance fighters, the anonymous use of cloud services on the Internet can be vital for survival. The Tor network provides a free and widely used anonymization service for everyone. However, there are different approaches to distinguishing Tor from non-Tor encrypted network traffic, most recently only due to the (relative) frequencies of hex digits in a single encrypted payload packet. While conventional data traffic is usually encrypted once, but at least three times in the case of Tor due to the structure and principle of the Tor network, we have examined to what extent the number of encryptions contributes to being able to distinguish Tor from non-Tor encrypted data traffic. | [
"['Pitpimon Choorod' 'Tobias J. Bauer' 'Andreas Aßmuth']"
]
|
null | null | 2405.09453 | null | null | http://arxiv.org/pdf/2405.09453v1 | 2024-05-15T15:48:11Z | 2024-05-15T15:48:11Z | Kuramoto Oscillators and Swarms on Manifolds for Geometry Informed
Machine Learning | We propose the idea of using Kuramoto models (including their higher-dimensional generalizations) for machine learning over non-Euclidean data sets. These models are systems of matrix ODE's describing collective motions (swarming dynamics) of abstract particles (generalized oscillators) on spheres, homogeneous spaces and Lie groups. Such models have been extensively studied from the beginning of XXI century both in statistical physics and control theory. They provide a suitable framework for encoding maps between various manifolds and are capable of learning over spherical and hyperbolic geometries. In addition, they can learn coupled actions of transformation groups (such as special orthogonal, unitary and Lorentz groups). Furthermore, we overview families of probability distributions that provide appropriate statistical models for probabilistic modeling and inference in Geometric Deep Learning. We argue in favor of using statistical models which arise in different Kuramoto models in the continuum limit of particles. The most convenient families of probability distributions are those which are invariant with respect to actions of certain symmetry groups. | [
"['Vladimir Jacimovic']"
]
|
null | null | 2405.09470 | null | null | http://arxiv.org/pdf/2405.09470v1 | 2024-05-15T16:05:24Z | 2024-05-15T16:05:24Z | Towards Evaluating the Robustness of Automatic Speech Recognition
Systems via Audio Style Transfer | In light of the widespread application of Automatic Speech Recognition (ASR) systems, their security concerns have received much more attention than ever before, primarily due to the susceptibility of Deep Neural Networks. Previous studies have illustrated that surreptitiously crafting adversarial perturbations enables the manipulation of speech recognition systems, resulting in the production of malicious commands. These attack methods mostly require adding noise perturbations under $ell_p$ norm constraints, inevitably leaving behind artifacts of manual modifications. Recent research has alleviated this limitation by manipulating style vectors to synthesize adversarial examples based on Text-to-Speech (TTS) synthesis audio. However, style modifications based on optimization objectives significantly reduce the controllability and editability of audio styles. In this paper, we propose an attack on ASR systems based on user-customized style transfer. We first test the effect of Style Transfer Attack (STA) which combines style transfer and adversarial attack in sequential order. And then, as an improvement, we propose an iterative Style Code Attack (SCA) to maintain audio quality. Experimental results show that our method can meet the need for user-customized styles and achieve a success rate of 82% in attacks, while keeping sound naturalness due to our user study. | [
"['Weifei Jin' 'Yuxin Cao' 'Junjie Su' 'Qi Shen' 'Kai Ye' 'Derui Wang'\n 'Jie Hao' 'Ziyao Liu']"
]
|
null | null | 2405.09477 | null | null | http://arxiv.org/pdf/2405.09477v1 | 2024-05-15T16:16:37Z | 2024-05-15T16:16:37Z | Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing
Knowledge Graph Task | Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention has been paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAIT, paving the way towards more effective and insightful KG analysis techniques. | [
"['Shurong Wang' 'Yufei Zhang' 'Xuliang Huang' 'Hongwei Wang']"
]
|
null | null | 2405.09483 | null | null | http://arxiv.org/pdf/2405.09483v2 | 2024-05-20T14:34:32Z | 2024-05-15T16:22:46Z | DemOpts: Fairness corrections in COVID-19 case prediction models | COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the art deep learning models often use multimodal data such as mobility or socio-demographic data to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which could in turn affect the fairness of the COVID-19 predictions along race labels. In this paper, we show that state of the art deep learning models output mean prediction errors that are significantly different across racial and ethnic groups; and which could, in turn, support unfair policy decisions. We also propose a novel de-biasing method, DemOpts, to increase the fairness of deep learning based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity that other state of the art de-biasing approaches, thus effectively reducing the differences in the mean error distributions across more racial and ethnic groups. | [
"['Naman Awasthi' 'Saad Abrar' 'Daniel Smolyak' 'Vanessa Frias-Martinez']"
]
|
null | null | 2405.09492 | null | null | http://arxiv.org/pdf/2405.09492v1 | 2024-05-15T16:37:09Z | 2024-05-15T16:37:09Z | MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware
Optimization for Enhanced Continual Learning | Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay-based algorithm specifically engineered to enhance the generalization capabilities of CL models. We first intergrate the SAM optimizer, a component designed for optimizing flatness, which seamlessly fits into well-known Experience Replay frameworks such as ER and DER++. Then, MGSER-SAM distinctively addresses the complex challenge of reconciling conflicts in weight perturbation directions between ongoing tasks and previously stored memories, which is underexplored in the SAM optimizer. This is effectively accomplished by the strategic integration of soft logits and the alignment of memory gradient directions, where the regularization terms facilitate the concurrent minimization of various training loss terms integral to the CL process. Through rigorous experimental analysis conducted across multiple benchmarks, MGSER-SAM has demonstrated a consistent ability to outperform existing baselines in all three CL scenarios. Comparing to the representative memory replay-based baselines ER and DER++, MGSER-SAM not only improves the testing accuracy by $24.4%$ and $17.6%$ respectively, but also achieves the lowest forgetting on each benchmark. | [
"['Xingyu Li' 'Bo Tang']"
]
|
null | null | 2405.09493 | null | null | http://arxiv.org/pdf/2405.09493v2 | 2024-05-22T05:45:43Z | 2024-05-15T16:38:28Z | C-Learner: Constrained Learning for Causal Inference and Semiparametric
Statistics | Causal estimation (e.g. of the average treatment effect) requires estimating complex nuisance parameters (e.g. outcome models). To adjust for errors in nuisance parameter estimation, we present a novel correction method that solves for the best plug-in estimator under the constraint that the first-order error of the estimator with respect to the nuisance parameter estimate is zero. Our constrained learning framework provides a unifying perspective to prominent first-order correction approaches including one-step estimation (a.k.a. augmented inverse probability weighting) and targeting (a.k.a. targeted maximum likelihood estimation). Our semiparametric inference approach, which we call the "C-Learner", can be implemented with modern machine learning methods such as neural networks and tree ensembles, and enjoys standard guarantees like semiparametric efficiency and double robustness. Empirically, we demonstrate our approach on several datasets, including those with text features that require fine-tuning language models. We observe the C-Learner matches or outperforms other asymptotically optimal estimators, with better performance in settings with less estimated overlap. | [
"['Tiffany Tianhui Cai' 'Yuri Fonseca' 'Kaiwen Hou' 'Hongseok Namkoong']"
]
|
null | null | 2405.09508 | null | null | http://arxiv.org/pdf/2405.09508v1 | 2024-05-15T17:01:02Z | 2024-05-15T17:01:02Z | Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer
Architectures for Cross-Language Structural Priming | This study evaluates the performance of Recurrent Neural Network (RNN) and Transformer in replicating cross-language structural priming: a key indicator of abstract grammatical representations in human language processing. Focusing on Chinese-English priming, which involves two typologically distinct languages, we examine how these models handle the robust phenomenon of structural priming, where exposure to a particular sentence structure increases the likelihood of selecting a similar structure subsequently. Additionally, we utilize large language models (LLM) to measure the cross-lingual structural priming effect. Our findings indicate that Transformer outperform RNN in generating primed sentence structures, challenging the conventional belief that human sentence processing primarily involves recurrent and immediate processing and suggesting a role for cue-based retrieval mechanisms. Overall, this work contributes to our understanding of how computational models may reflect human cognitive processes in multilingual contexts. | [
"['Bushi Xiao' 'Chao Gao' 'Demi Zhang']"
]
|
null | null | 2405.09514 | null | null | http://arxiv.org/pdf/2405.09514v1 | 2024-05-15T17:07:55Z | 2024-05-15T17:07:55Z | Tackling Distribution Shifts in Task-Oriented Communication with
Information Bottleneck | Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between training and test data, including domain shift and semantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that the encoded features can generalize to domain-shifted data and detect semanticshifted data, while remaining compact for transmission. In this paper, we propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effective domain-shift generalization and accurate semantic-shift detection without any knowledge of the test data during training. Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature. Furthermore, we enhance the task-oriented communication with the label-dependent feature encoding approach for semanticshift detection which achieves joint gains in IB optimization and detection performance. To avoid the intractable computation of the IB-based objective, we leverage variational approximation to derive a tractable upper bound for optimization. Extensive simulation results on image classification tasks demonstrate that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff. | [
"['Hongru Li' 'Jiawei Shao' 'Hengtao He' 'Shenghui Song' 'Jun Zhang'\n 'Khaled B. Letaief']"
]
|
null | null | 2405.09516 | null | null | http://arxiv.org/pdf/2405.09516v1 | 2024-05-15T17:17:27Z | 2024-05-15T17:17:27Z | Generalization Bounds for Causal Regression: Insights, Guarantees and
Sensitivity Analysis | Many algorithms have been recently proposed for causal machine learning. Yet, there is little to no theory on their quality, especially considering finite samples. In this work, we propose a theory based on generalization bounds that provides such guarantees. By introducing a novel change-of-measure inequality, we are able to tightly bound the model loss in terms of the deviation of the treatment propensities over the population, which we show can be empirically limited. Our theory is fully rigorous and holds even in the face of hidden confounding and violations of positivity. We demonstrate our bounds on semi-synthetic and real data, showcasing their remarkable tightness and practical utility. | [
"['Daniel Csillag' 'Claudio José Struchiner' 'Guilherme Tegoni Goedert']"
]
|
null | null | 2405.09522 | null | null | http://arxiv.org/abs/2405.09522v2 | 2024-05-24T11:51:32Z | 2024-05-15T17:25:59Z | ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment
Simulations | Learning-based approaches to cloth simulation have started to show their potential in recent years. However, handling collisions and intersections in neural simulations remains a largely unsolved problem. In this work, we present moniker{}, a learning-based solution for handling intersections in neural cloth simulations. Unlike conventional approaches that critically rely on intersection-free inputs, moniker{} robustly recovers from intersections introduced through missed collisions, self-penetrating bodies, or errors in manually designed multi-layer outfits. The technical core of moniker{} is a novel intersection contour loss that penalizes interpenetrations and encourages rapid resolution thereof. We integrate our intersection loss with a collision-avoiding repulsion objective into a neural cloth simulation method based on graph neural networks (GNNs). We demonstrate our method's ability across a challenging set of diverse multi-layer outfits under dynamic human motions. Our extensive analysis indicates that moniker{} significantly improves collision handling for learned simulation and produces visually compelling results. | [
"['Artur Grigorev' 'Giorgio Becherini' 'Michael J. Black' 'Otmar Hilliges'\n 'Bernhard Thomaszewski']"
]
|
null | null | 2405.09525 | null | null | http://arxiv.org/pdf/2405.09525v1 | 2024-05-15T17:33:10Z | 2024-05-15T17:33:10Z | Improved classical shadows from local symmetries in the Schur basis | We study the sample complexity of the classical shadows task: what is the fewest number of copies of an unknown state you need to measure to predict expected values with respect to some class of observables? Large joint measurements are likely required in order to minimize sample complexity, but previous joint measurement protocols only work when the unknown state is pure. We present the first joint measurement protocol for classical shadows whose sample complexity scales with the rank of the unknown state. In particular we prove $mathcal O(sqrt{rB}/epsilon^2)$ samples suffice, where $r$ is the rank of the state, $B$ is a bound on the squared Frobenius norm of the observables, and $epsilon$ is the target accuracy. In the low-rank regime, this is a nearly quadratic advantage over traditional approaches that use single-copy measurements. We present several intermediate results that may be of independent interest: a solution to a new formulation of classical shadows that captures functions of non-identical input states; a generalization of a ``nice'' Schur basis used for optimal qubit purification and quantum majority vote; and a measurement strategy that allows us to use local symmetries in the Schur basis to avoid intractable Weingarten calculations in the analysis. | [
"['Daniel Grier' 'Sihan Liu' 'Gaurav Mahajan']"
]
|
null | null | 2405.09529 | null | null | http://arxiv.org/pdf/2405.09529v1 | 2024-04-02T09:59:23Z | 2024-04-02T09:59:23Z | Artificial Intelligence for the Internal Democracy of Political Parties | The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language processing and sentiment analysis, can improve the measurement (ML about) and practice (ML for) of IPD. The article concludes by considering some of the principal risks of ML for IPD, including concerns over data privacy, the potential for manipulation, and the dangers of overreliance on technology. | [
"['Claudio Novelli' 'Giuliano Formisano' 'Prathm Juneja' 'Giulia Sandri'\n 'Luciano Floridi']"
]
|
null | null | 2405.09530 | null | null | http://arxiv.org/pdf/2405.09530v1 | 2024-05-01T15:18:01Z | 2024-05-01T15:18:01Z | A community palm model | Palm oil production has been identified as one of the major drivers of deforestation for tropical countries. To meet supply chain objectives, commodity producers and other stakeholders need timely information of land cover dynamics in their supply shed. However, such data are difficult to obtain from suppliers who may lack digital geographic representations of their supply sheds and production locations. Here we present a "community model," a machine learning model trained on pooled data sourced from many different stakeholders, to develop a specific land cover probability map, in this case a semi-global oil palm map. An advantage of this method is the inclusion of varied inputs, the ability to easily update the model as new training data becomes available and run the model on any year that input imagery is available. Inclusion of diverse data sources into one probability map can help establish a shared understanding across stakeholders on the presence and absence of a land cover or commodity (in this case oil palm). The model predictors are annual composites built from publicly available satellite imagery provided by Sentinel-1, Sentinel-2, and ALOS DSM. We provide map outputs as the probability of palm in a given pixel, to reflect the uncertainty of the underlying state (palm or not palm). The initial version of this model provides global accuracy estimated to be approximately 90% (at 0.5 probability threshold) from spatially partitioned test data. This model, and resulting oil palm probability map products are useful for accurately identifying the geographic footprint of palm cultivation. Used in conjunction with timely deforestation information, this palm model is useful for understanding the risk of continued oil palm plantation expansion in sensitive forest areas. | [
"['Nicholas Clinton' 'Andreas Vollrath' \"Remi D'annunzio\" 'Desheng Liu'\n 'Henry B. Glick' 'Adrià Descals' 'Alicia Sullivan' 'Oliver Guinan'\n 'Jacob Abramowitz' 'Fred Stolle' 'Chris Goodman' 'Tanya Birch'\n 'David Quinn' 'Olga Danylo' 'Tijs Lips' 'Daniel Coelho' 'Enikoe Bihari'\n 'Bryce Cronkite-Ratcliff' 'Ate Poortinga' 'Atena Haghighattalab'\n 'Evan Notman' 'Michael DeWitt' 'Aaron Yonas' 'Gennadii Donchyts'\n 'Devaja Shah' 'David Saah' 'Karis Tenneson' 'Nguyen Hanh Quyen'\n 'Megha Verma' 'Andrew Wilcox']"
]
|
null | null | 2405.09535 | null | null | http://arxiv.org/pdf/2405.09535v1 | 2024-05-15T17:45:34Z | 2024-05-15T17:45:34Z | Restoring balance: principled under/oversampling of data for optimal
classification | Class imbalance in real-world data poses a common bottleneck for machine learning tasks, since achieving good generalization on under-represented examples is often challenging. Mitigation strategies, such as under or oversampling the data depending on their abundances, are routinely proposed and tested empirically, but how they should adapt to the data statistics remains poorly understood. In this work, we determine exact analytical expressions of the generalization curves in the high-dimensional regime for linear classifiers (Support Vector Machines). We also provide a sharp prediction of the effects of under/oversampling strategies depending on class imbalance, first and second moments of the data, and the metrics of performance considered. We show that mixed strategies involving under and oversampling of data lead to performance improvement. Through numerical experiments, we show the relevance of our theoretical predictions on real datasets, on deeper architectures and with sampling strategies based on unsupervised probabilistic models. | [
"['Emanuele Loffredo' 'Mauro Pastore' 'Simona Cocco' 'Rémi Monasson']"
]
|
null | null | 2405.09536 | null | null | http://arxiv.org/pdf/2405.09536v1 | 2024-05-15T17:45:59Z | 2024-05-15T17:45:59Z | Wasserstein Gradient Boosting: A General Framework with Applications to
Posterior Regression | Gradient boosting is a sequential ensemble method that fits a new base learner to the gradient of the remaining loss at each step. We propose a novel family of gradient boosting, Wasserstein gradient boosting, which fits a new base learner to an exactly or approximately available Wasserstein gradient of a loss functional on the space of probability distributions. Wasserstein gradient boosting returns a set of particles that approximates a target probability distribution assigned at each input. In probabilistic prediction, a parametric probability distribution is often specified on the space of output variables, and a point estimate of the output-distribution parameter is produced for each input by a model. Our main application of Wasserstein gradient boosting is a novel distributional estimate of the output-distribution parameter, which approximates the posterior distribution over the output-distribution parameter determined pointwise at each data point. We empirically demonstrate the superior performance of the probabilistic prediction by Wasserstein gradient boosting in comparison with various existing methods. | [
"['Takuo Matsubara']"
]
|
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