categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.07776 | null | null | http://arxiv.org/pdf/2404.07776v1 | 2024-04-11T14:13:53Z | 2024-04-11T14:13:53Z | Unsupervised Concept Drift Detection based on Parallel Activations of
Neural Network | Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration - resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate access to labels, which due to their cost, limited availability and possible delay has been shown to be unrealistic. This work proposes an unsupervised Parallel Activations Drift Detector, utilizing the outputs of an untrained neural network, presenting its key design elements, intuitions about processing properties, and a pool of computer experiments demonstrating its competitiveness with state-of-the-art methods. | [
"['Joanna Komorniczak' 'Paweł Ksieniewicz']"
]
|
null | null | 2404.07792 | null | null | http://arxiv.org/pdf/2404.07792v1 | 2024-04-11T14:35:23Z | 2024-04-11T14:35:23Z | Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection
through Data Augmentation | This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two methods for doing so on the basis of the $k$-means algorithm, and we employ a variety of Latin large language models (LLMs) in a neural architecture to better capture the underlying contextual sentiment representations. Our best approach achieved the second highest macro-averaged Macro-$F_1$ score on the shared task's test set. | [
"['Stephen Bothwell' 'Abigail Swenor' 'David Chiang']"
]
|
null | null | 2404.07815 | null | null | http://arxiv.org/pdf/2404.07815v1 | 2024-04-11T14:58:19Z | 2024-04-11T14:58:19Z | Post-Hoc Reversal: Are We Selecting Models Prematurely? | Trained models are often composed with post-hoc transforms such as temperature scaling (TS), ensembling and stochastic weight averaging (SWA) to improve performance, robustness, uncertainty estimation, etc. However, such transforms are typically applied only after the base models have already been finalized by standard means. In this paper, we challenge this practice with an extensive empirical study. In particular, we demonstrate a phenomenon that we call post-hoc reversal, where performance trends are reversed after applying these post-hoc transforms. This phenomenon is especially prominent in high-noise settings. For example, while base models overfit badly early in training, both conventional ensembling and SWA favor base models trained for more epochs. Post-hoc reversal can also suppress the appearance of double descent and mitigate mismatches between test loss and test error seen in base models. Based on our findings, we propose post-hoc selection, a simple technique whereby post-hoc metrics inform model development decisions such as early stopping, checkpointing, and broader hyperparameter choices. Our experimental analyses span real-world vision, language, tabular and graph datasets from domains like satellite imaging, language modeling, census prediction and social network analysis. On an LLM instruction tuning dataset, post-hoc selection results in > 1.5x MMLU improvement compared to naive selection. Code is available at https://github.com/rishabh-ranjan/post-hoc-reversal. | [
"['Rishabh Ranjan' 'Saurabh Garg' 'Mrigank Raman' 'Carlos Guestrin'\n 'Zachary Chase Lipton']"
]
|
null | null | 2404.07817 | null | null | http://arxiv.org/pdf/2404.07817v2 | 2024-04-12T12:33:26Z | 2024-04-11T14:59:49Z | Calibration of Continual Learning Models | Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction. | [
"['Lanpei Li' 'Elia Piccoli' 'Andrea Cossu' 'Davide Bacciu'\n 'Vincenzo Lomonaco']"
]
|
null | null | 2404.07826 | null | null | http://arxiv.org/pdf/2404.07826v1 | 2024-04-11T15:09:49Z | 2024-04-11T15:09:49Z | On the Sample Efficiency of Abstractions and Potential-Based Reward
Shaping in Reinforcement Learning | The use of Potential Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, the choice of the potential function is critical for this technique to be effective. Additionally, RL techniques are usually constrained to use a finite horizon for computational limitations. This introduces a bias when using PBRS, thus adding an additional layer of complexity. In this paper, we leverage abstractions to automatically produce a "good" potential function. We analyse the bias induced by finite horizons in the context of PBRS producing novel insights. Finally, to asses sample efficiency and performance impact, we evaluate our approach on four environments including a goal-oriented navigation task and three Arcade Learning Environments (ALE) games demonstrating that we can reach the same level of performance as CNN-based solutions with a simple fully-connected network. | [
"['Giuseppe Canonaco' 'Leo Ardon' 'Alberto Pozanco' 'Daniel Borrajo']"
]
|
null | null | 2404.07833 | null | null | http://arxiv.org/pdf/2404.07833v1 | 2024-04-11T15:18:34Z | 2024-04-11T15:18:34Z | Streamlined Photoacoustic Image Processing with Foundation Models: A
Training-Free Solution | Foundation models have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we propose a method based on foundation models and zero training to solve the tasks of photoacoustic (PA) image segmentation. We employed the segment anything model (SAM) by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that deep learning can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This letter serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets. | [
"['Handi Deng' 'Yucheng Zhou' 'Jiaxuan Xiang' 'Liujie Gu' 'Yan Luo'\n 'Hai Feng' 'Mingyuan Liu' 'Cheng Ma']"
]
|
null | null | 2404.07839 | null | null | http://arxiv.org/pdf/2404.07839v1 | 2024-04-11T15:27:22Z | 2024-04-11T15:27:22Z | RecurrentGemma: Moving Past Transformers for Efficient Open Language
Models | We introduce RecurrentGemma, an open language model which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens. | [
"['Aleksandar Botev' 'Soham De' 'Samuel L Smith' 'Anushan Fernando'\n 'George-Cristian Muraru' 'Ruba Haroun' 'Leonard Berrada' 'Razvan Pascanu'\n 'Pier Giuseppe Sessa' 'Robert Dadashi' 'Léonard Hussenot' 'Johan Ferret'\n 'Sertan Girgin' 'Olivier Bachem' 'Alek Andreev' 'Kathleen Kenealy'\n 'Thomas Mesnard' 'Cassidy Hardin' 'Surya Bhupatiraju' 'Shreya Pathak'\n 'Laurent Sifre' 'Morgane Rivière' 'Mihir Sanjay Kale' 'Juliette Love'\n 'Pouya Tafti' 'Armand Joulin' 'Noah Fiedel' 'Evan Senter' 'Yutian Chen'\n 'Srivatsan Srinivasan' 'Guillaume Desjardins' 'David Budden'\n 'Arnaud Doucet' 'Sharad Vikram' 'Adam Paszke' 'Trevor Gale'\n 'Sebastian Borgeaud' 'Charlie Chen' 'Andy Brock' 'Antonia Paterson'\n 'Jenny Brennan' 'Meg Risdal' 'Raj Gundluru' 'Nesh Devanathan'\n 'Paul Mooney' 'Nilay Chauhan' 'Phil Culliton' 'Luiz GUStavo Martins'\n 'Elisa Bandy' 'David Huntsperger' 'Glenn Cameron' 'Arthur Zucker'\n 'Tris Warkentin' 'Ludovic Peran' 'Minh Giang' 'Zoubin Ghahramani'\n 'Clément Farabet' 'Koray Kavukcuoglu' 'Demis Hassabis' 'Raia Hadsell'\n 'Yee Whye Teh' 'Nando de Frietas']"
]
|
null | null | 2404.07840 | null | null | http://arxiv.org/pdf/2404.07840v2 | 2024-04-16T10:05:27Z | 2024-04-11T15:27:56Z | On Training Data Influence of GPT Models | Amidst the rapid advancements in generative language models, the investigation of how training data shapes the performance of GPT models is still emerging. This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models. Our approach not only traces the influence of individual training instances on performance trajectories, such as loss and other key metrics, on targeted test points but also enables a comprehensive comparison with existing methods across various training scenarios in GPT models, ranging from 14 million to 2.8 billion parameters, across a range of downstream tasks. Contrary to earlier methods that struggle with generalization to new data, GPTfluence introduces a parameterized simulation of training dynamics, demonstrating robust generalization capabilities to unseen training data. This adaptability is evident across both fine-tuning and instruction-tuning scenarios, spanning tasks in natural language understanding and generation. We will make our code and data publicly available. | [
"['Qingyi Liu' 'Yekun Chai' 'Shuohuan Wang' 'Yu Sun' 'Qiwei Peng'\n 'Keze Wang' 'Hua Wu']"
]
|
null | null | 2404.07849 | null | null | http://arxiv.org/pdf/2404.07849v1 | 2024-04-11T15:43:11Z | 2024-04-11T15:43:11Z | Overparameterized Multiple Linear Regression as Hyper-Curve Fitting | The paper shows that the application of the fixed-effect multiple linear regression model to an overparameterized dataset is equivalent to fitting the data with a hyper-curve parameterized by a single scalar parameter. This equivalence allows for a predictor-focused approach, where each predictor is described by a function of the chosen parameter. It is proven that a linear model will produce exact predictions even in the presence of nonlinear dependencies that violate the model assumptions. Parameterization in terms of the dependent variable and the monomial basis in the predictor function space are applied here to both synthetic and experimental data. The hyper-curve approach is especially suited for the regularization of problems with noise in predictor variables and can be used to remove noisy and "improper" predictors from the model. | [
"['E. Atza' 'N. Budko']"
]
|
null | null | 2404.07860 | null | null | http://arxiv.org/abs/2404.07860v1 | 2024-04-11T15:54:20Z | 2024-04-11T15:54:20Z | Streaming detection of significant delay changes in public transport
systems | Public transport systems are expected to reduce pollution and contribute to sustainable development. However, disruptions in public transport such as delays may negatively affect mobility choices. To quantify delays, aggregated data from vehicle locations systems are frequently used. However, delays observed at individual stops are caused inter alia by fluctuations in running times and propagation of delays occurring in other locations. Hence, in this work, we propose both the method detecting significant delays and reference architecture, relying on stream processing engines, in which the method is implemented. The method can complement the calculation of delays defined as deviation from schedules. This provides both online rather than batch identification of significant and repetitive delays, and resilience to the limited quality of location data. The method we propose can be used with different change detectors, such as ADWIN, applied to location data stream shuffled to individual edges of a transport graph. It can detect in an online manner at which edges statistically significant delays are observed and at which edges delays arise and are reduced. Detections can be used to model mobility choices and quantify the impact of repetitive rather than random disruptions on feasible trips with multimodal trip modelling engines. The evaluation performed with the public transport data of over 2000 vehicles confirms the merits of the method and reveals that a limited-size subgraph of a transport system graph causes statistically significant delays | [
"['Przemysław Wrona' 'Maciej Grzenda' 'Marcin Luckner']"
]
|
null | null | 2404.07864 | null | null | http://arxiv.org/pdf/2404.07864v1 | 2024-04-11T15:57:12Z | 2024-04-11T15:57:12Z | Inferring Change Points in High-Dimensional Linear Regression via
Approximate Message Passing | We consider the problem of localizing change points in high-dimensional linear regression. We propose an Approximate Message Passing (AMP) algorithm for estimating both the signals and the change point locations. Assuming Gaussian covariates, we give an exact asymptotic characterization of its estimation performance in the limit where the number of samples grows proportionally to the signal dimension. Our algorithm can be tailored to exploit any prior information on the signal, noise, and change points. It also enables uncertainty quantification in the form of an efficiently computable approximate posterior distribution, whose asymptotic form we characterize exactly. We validate our theory via numerical experiments, and demonstrate the favorable performance of our estimators on both synthetic data and images. | [
"['Gabriel Arpino' 'Xiaoqi Liu' 'Ramji Venkataramanan']"
]
|
null | null | 2404.07898 | null | null | http://arxiv.org/pdf/2404.07898v1 | 2024-04-11T16:37:01Z | 2024-04-11T16:37:01Z | Anomaly Detection in Power Grids via Context-Agnostic Learning | An important tool grid operators use to safeguard against failures, whether naturally occurring or malicious, involves detecting anomalies in the power system SCADA data. In this paper, we aim to solve a real-time anomaly detection problem. Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the network topology or measurement data? Existing methods, primarily optimization-based, mostly use only a single snapshot of the measurement values and do not scale well with the network size. Recent data-driven ML techniques have shown promise by using a combination of current and historical data for anomaly detection but generally do not consider physical attributes like the impact of topology or load/generation changes on sensor measurements and thus cannot accommodate regular context-variability in the historical data. To address this gap, we propose a novel context-aware anomaly detection algorithm, GridCAL, that considers the effect of regular topology and load/generation changes. This algorithm converts the real-time power flow measurements to context-agnostic values, which allows us to analyze measurement coming from different grid contexts in an aggregate fashion, enabling us to derive a unified statistical model that becomes the basis of anomaly detection. Through numerical simulations on networks up to 2383 nodes, we show that our approach is accurate, outperforming state-of-the-art approaches, and is computationally efficient. | [
"['SangWoo Park' 'Amritanshu Pandey']"
]
|
null | null | 2404.07919 | null | null | http://arxiv.org/pdf/2404.07919v1 | 2024-04-11T17:04:55Z | 2024-04-11T17:04:55Z | Low-rank Adaptation for Spatio-Temporal Forecasting | Spatio-temporal forecasting is crucial in real-world dynamic systems, predicting future changes using historical data from diverse locations. Existing methods often prioritize the development of intricate neural networks to capture the complex dependencies of the data, yet their accuracy fails to show sustained improvement. Besides, these methods also overlook node heterogeneity, hindering customized prediction modules from handling diverse regional nodes effectively. In this paper, our goal is not to propose a new model but to present a novel low-rank adaptation framework as an off-the-shelf plugin for existing spatial-temporal prediction models, termed ST-LoRA, which alleviates the aforementioned problems through node-level adjustments. Specifically, we first tailor a node adaptive low-rank layer comprising multiple trainable low-rank matrices. Additionally, we devise a multi-layer residual fusion stacking module, injecting the low-rank adapters into predictor modules of various models. Across six real-world traffic datasets and six different types of spatio-temporal prediction models, our approach minimally increases the parameters and training time of the original models by less than 4%, still achieving consistent and sustained performance enhancement. | [
"['Weilin Ruan' 'Wei Chen' 'Xilin Dang' 'Jianxiang Zhou' 'Weichuang Li'\n 'Xu Liu' 'Yuxuan Liang']"
]
|
null | null | 2404.07922 | null | null | http://arxiv.org/pdf/2404.07922v5 | 2024-05-26T10:27:51Z | 2024-04-11T17:09:28Z | LaVy: Vietnamese Multimodal Large Language Model | Large Language Models (LLMs) and Multimodal Large language models (MLLMs) have taken the world by storm with impressive abilities in complex reasoning and linguistic comprehension. Meanwhile there are plethora of works related to Vietnamese Large Language Models, the lack of high-quality resources in multimodality limits the progress of Vietnamese MLLMs. In this paper, we pioneer in address this by introducing LaVy, a state-of-the-art Vietnamese MLLM, and we also introduce LaVy-Bench benchmark designated for evaluating MLLMs's understanding on Vietnamese visual language tasks. Our project is public at https://github.com/baochi0212/LaVy | [
"['Chi Tran' 'Huong Le Thanh']"
]
|
null | null | 2404.07924 | null | null | http://arxiv.org/pdf/2404.07924v1 | 2024-04-11T17:10:57Z | 2024-04-11T17:10:57Z | A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM | Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runoff and time series settings, CNN-LSTMs have primarily been utilized in rainfall-runoff scenarios. In this study, we extend the application of CNN-LSTMs to time series settings, leveraging lagged streamflow data in conjunction with precipitation and temperature data to predict streamflow. Our results show a substantial improvement in predictive performance in 21 out of 32 HUC8 basins in Nebraska, showcasing noteworthy increases in the Kling-Gupta Efficiency (KGE) values. These results highlight the effectiveness of CNN-LSTMs in time series settings, particularly for spatiotemporal hydrological modeling, for more accurate and robust streamflow predictions. | [
"['Sudan Pokharel' 'Tirthankar Roy']"
]
|
null | null | 2404.07937 | null | null | http://arxiv.org/pdf/2404.07937v2 | 2024-04-15T21:12:20Z | 2024-04-11T17:36:28Z | Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method | We study the quadratic prediction error method -- i.e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability condition. While this method is known to asymptotically achieve the optimal rate for a wide range of problems, there have been no non-asymptotic results matching these optimal rates outside of a select few, typically linear, model classes. By leveraging modern tools from learning with dependent data, we provide the first rate-optimal non-asymptotic analysis of this method for our more general setting of nonlinearly parametrized model classes. Moreover, we show that our results can be applied to a particular class of identifiable AutoRegressive Moving Average (ARMA) models, resulting in the first optimal non-asymptotic rates for identification of ARMA models. | [
"['Charis Stamouli' 'Ingvar Ziemann' 'George J. Pappas']"
]
|
null | null | 2404.07939 | null | null | http://arxiv.org/pdf/2404.07939v1 | 2024-03-09T05:18:15Z | 2024-03-09T05:18:15Z | Distributed Record Linkage in Healthcare Data with Apache Spark | Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze effectively. Record linkage, also known as data matching, is a crucial step in integrating and cleaning healthcare data to ensure data quality and accuracy. Apache Spark, a powerful open-source distributed big data processing framework, provides a robust platform for performing record linkage tasks with the aid of its machine learning library. In this study, we developed a new distributed data-matching model based on the Apache Spark Machine Learning library. To ensure the correct functioning of our model, the validation phase has been performed on the training data. The main challenge is data imbalance because a large amount of data is labeled false, and a small number of records are labeled true. By utilizing SVM and Regression algorithms, our results demonstrate that research data was neither over-fitted nor under-fitted, and this shows that our distributed model works well on the data. | [
"['Mohammad Heydari' 'Reza Sarshar' 'Mohammad Ali Soltanshahi']"
]
|
null | null | 2404.07940 | null | null | http://arxiv.org/pdf/2404.07940v2 | 2024-06-27T08:06:15Z | 2024-03-11T02:06:30Z | InfiBench: Evaluating the Question-Answering Capabilities of Code Large
Language Models | Large Language Models for code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-related questions. To fill this gap, we propose InfiBench, the first large-scale freeform question-answering (QA) benchmark for code to our knowledge, comprising 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. InfiBench uses four types of model-free automatic metrics to evaluate response correctness where domain experts carefully concretize the criterion for each question. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings. Our detailed analyses showcase potential directions for further advancement of code LLMs. InfiBench is fully open source and continuously expanding to foster more scientific and systematic practices for code LLM evaluation. | [
"['Linyi Li' 'Shijie Geng' 'Zhenwen Li' 'Yibo He' 'Hao Yu' 'Ziyue Hua'\n 'Guanghan Ning' 'Siwei Wang' 'Tao Xie' 'Hongxia Yang']"
]
|
null | null | 2404.07941 | null | null | http://arxiv.org/pdf/2404.07941v1 | 2024-03-11T05:19:43Z | 2024-03-11T05:19:43Z | SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph
Representation Learning | In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal processing in DGRL task. However, owing to the spike-based information encoding mechanism of SNNs, existing DGRL methods employed SNNs face limitations in their representational capacity. Given this issue, we propose a novel framework named Spike-induced Graph Neural Network (SiGNN) for learning enhanced spatialtemporal representations on dynamic graphs. In detail, a harmonious integration of SNNs and GNNs is achieved through an innovative Temporal Activation (TA) mechanism. Benefiting from the TA mechanism, SiGNN not only effectively exploits the temporal dynamics of SNNs but also adeptly circumvents the representational constraints imposed by the binary nature of spikes. Furthermore, leveraging the inherent adaptability of SNNs, we explore an in-depth analysis of the evolutionary patterns within dynamic graphs across multiple time granularities. This approach facilitates the acquisition of a multiscale temporal node representation.Extensive experiments on various real-world dynamic graph datasets demonstrate the superior performance of SiGNN in the node classification task. | [
"['Dong Chen' 'Shuai Zheng' 'Muhao Xu' 'Zhenfeng Zhu' 'Yao Zhao']"
]
|
null | null | 2404.07943 | null | null | http://arxiv.org/pdf/2404.07943v1 | 2024-03-18T06:47:35Z | 2024-03-18T06:47:35Z | HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of
Effective Mechanical Properties using Neural Operators | Homogenization is an essential tool for studying multiscale physical phenomena. However, traditional numerical homogenization, heavily reliant on finite element analysis, requires extensive computation costs, particularly in handling complex geometries, materials, and high-resolution problems. To address these limitations, we propose a numerical homogenization model based on operator learning: HomoGenius. The proposed model can quickly provide homogenization results for arbitrary geometries, materials, and resolutions, increasing the efficiency by a factor of 80 compared to traditional numerical homogenization methods. We validate effectiveness of our model in predicting the effective elastic modulus on periodic materials (TPMS: Triply Periodic Minimal Surface), including complex geometries, various Poisson's ratios and elastic modulus, and different resolutions for training and testing. The results show that our model possesses high precision, super efficiency, and learning capability. | [
"['Yizheng Wang' 'Xiang Li' 'Ziming Yan' 'Yuqing Du' 'Jinshuai Bai'\n 'Bokai Liu' 'Timon Rabczuk' 'Yinghua Liu']"
]
|
null | null | 2404.07946 | null | null | http://arxiv.org/pdf/2404.07946v1 | 2024-03-14T13:27:04Z | 2024-03-14T13:27:04Z | Towards Faster Training of Diffusion Models: An Inspiration of A
Consistency Phenomenon | Diffusion models (DMs) are a powerful generative framework that have attracted significant attention in recent years. However, the high computational cost of training DMs limits their practical applications. In this paper, we start with a consistency phenomenon of DMs: we observe that DMs with different initializations or even different architectures can produce very similar outputs given the same noise inputs, which is rare in other generative models. We attribute this phenomenon to two factors: (1) the learning difficulty of DMs is lower when the noise-prediction diffusion model approaches the upper bound of the timestep (the input becomes pure noise), where the structural information of the output is usually generated; and (2) the loss landscape of DMs is highly smooth, which implies that the model tends to converge to similar local minima and exhibit similar behavior patterns. This finding not only reveals the stability of DMs, but also inspires us to devise two strategies to accelerate the training of DMs. First, we propose a curriculum learning based timestep schedule, which leverages the noise rate as an explicit indicator of the learning difficulty and gradually reduces the training frequency of easier timesteps, thus improving the training efficiency. Second, we propose a momentum decay strategy, which reduces the momentum coefficient during the optimization process, as the large momentum may hinder the convergence speed and cause oscillations due to the smoothness of the loss landscape. We demonstrate the effectiveness of our proposed strategies on various models and show that they can significantly reduce the training time and improve the quality of the generated images. | [
"['Tianshuo Xu' 'Peng Mi' 'Ruilin Wang' 'Yingcong Chen']"
]
|
null | null | 2404.07947 | null | null | http://arxiv.org/pdf/2404.07947v1 | 2024-03-15T06:21:56Z | 2024-03-15T06:21:56Z | ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference | This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference throughput while satisfying a given latency constraint. By leveraging the distribution of input and output sequences, it effectively allocates resources and determines optimal execution configurations, including batch sizes and partial tensor parallelism. We also introduce two scheduling strategies based on Round-Robin Allocation and Workload-Aware Allocation policies, suitable for different NLP workloads. We evaluate ExeGPT on six LLM instances of T5, OPT, and GPT-3 and five NLP tasks, each with four distinct latency constraints. Compared to FasterTransformer, ExeGPT achieves up to 15.2x improvements in throughput and 6x improvements in latency. Overall, ExeGPT achieves an average throughput gain of 2.9x across twenty evaluation scenarios. Moreover, when adapting to changing sequence distributions, the cost of adjusting the schedule in ExeGPT is reasonably modest. ExeGPT proves to be an effective solution for optimizing and executing LLM inference for diverse NLP workload and serving conditions. | [
"['Hyungjun Oh' 'Kihong Kim' 'Jaemin Kim' 'Sungkyun Kim' 'Junyeol Lee'\n 'Du-seong Chang' 'Jiwon Seo']"
]
|
null | null | 2404.07948 | null | null | http://arxiv.org/pdf/2404.07948v1 | 2024-03-18T09:26:04Z | 2024-03-18T09:26:04Z | Usability and Performance Analysis of Embedded Development Environment
for On-device Learning | This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic hardware manipulation to deployment of minimalistic ML training. The analysis encompasses memory usage, energy consumption, and performance metrics during model training and inference and usability of the different solutions. Arduino Framework offers ease of implementation but with increased energy consumption compared to the native option, while RIOT OS exhibits efficient energy consumption despite higher memory utilization with equivalent ease of use. The absence of certain critical functionalities like DVFS directly integrated into the OS highlights limitations for fine hardware control. | [
"['Enzo Scaffi' 'Antoine Bonneau' 'Frédéric Le Mouël' 'Fabien Mieyeville']"
]
|
null | null | 2404.07950 | null | null | http://arxiv.org/pdf/2404.07950v1 | 2024-03-18T16:50:23Z | 2024-03-18T16:50:23Z | Reinforcement Learning with Generalizable Gaussian Splatting | An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment representation directly influences the achievement of the learning task. Previous vision-based RL typically uses explicit or implicit ways to represent environments, such as images, points, voxels, and neural radiance fields. However, these representations contain several drawbacks. They cannot either describe complex local geometries or generalize well to unseen scenes, or require precise foreground masks. Moreover, these implicit neural representations are akin to a ``black box", significantly hindering interpretability. 3D Gaussian Splatting (3DGS), with its explicit scene representation and differentiable rendering nature, is considered a revolutionary change for reconstruction and representation methods. In this paper, we propose a novel Generalizable Gaussian Splatting framework to be the representation of RL tasks, called GSRL. Through validation in the RoboMimic environment, our method achieves better results than other baselines in multiple tasks, improving the performance by 10%, 44%, and 15% compared with baselines on the hardest task. This work is the first attempt to leverage generalizable 3DGS as a representation for RL. | [
"['Jiaxu Wang' 'Qiang Zhang' 'Jingkai Sun' 'Jiahang Cao' 'Yecheng Shao'\n 'Renjing Xu']"
]
|
null | null | 2404.07952 | null | null | http://arxiv.org/pdf/2404.07952v1 | 2024-03-19T14:08:56Z | 2024-03-19T14:08:56Z | Deep learning-based auto-segmentation of paraganglioma for growth
monitoring | Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for monitoring and modeling tumor growth in the long term. However, in clinical practice, using available tools to do these measurements is time-consuming and suffers from tumor-shape assumptions and observer-to-observer variation. Growth modeling could play a significant role in solving a decades-old dilemma (stemming from uncertainty regarding how the tumor will develop over time). By giving paraganglioma patients treatment, severe symptoms can be prevented. However, treating patients who do not actually need it, comes at the cost of unnecessary possible side effects and complications. Improved measurement techniques could enable growth model studies with a large amount of tumor volume data, possibly giving valuable insights into how these tumors develop over time. Therefore, we propose an automated tumor volume measurement method based on a deep learning segmentation model using no-new-UNnet (nnUNet). We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers. Our findings indicate that the automatic method performs (at least) equal to manual delineation. Finally, using the created model, and a linking procedure that we propose to track the tumor over time, we show how additional volume measurements affect the fit of known growth functions. | [
"['E. M. C. Sijben' 'J. C. Jansen' 'M. de Ridder' 'P. A. N. Bosman'\n 'T. Alderliesten']"
]
|
null | null | 2404.07954 | null | null | http://arxiv.org/pdf/2404.07954v1 | 2024-03-24T08:33:27Z | 2024-03-24T08:33:27Z | An efficient domain-independent approach for supervised keyphrase
extraction and ranking | We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too. Despite the supervised nature of our solution, the fact that does not rely on any corpus of "golden" keywords or any external knowledge corpus means that our solution bears the advantages of unsupervised solutions to a fair extent. | [
"['Sriraghavendra Ramaswamy']"
]
|
null | null | 2404.07955 | null | null | http://arxiv.org/pdf/2404.07955v1 | 2024-03-21T14:41:12Z | 2024-03-21T14:41:12Z | Triple Component Matrix Factorization: Untangling Global, Local, and
Noisy Components | In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by sparse and potentially gross noise, can we recover the common and unique components from these noisy observations? This is a challenging task as the number of parameters to estimate is approximately thrice the number of observations. Despite the difficulty, we propose an intuitive alternating minimization algorithm called triple component matrix factorization (TCMF) to recover the three components exactly. TCMF is distinguished from existing works in literature thanks to two salient features. First, TCMF is a principled method to separate the three components given noisy observations provably. Second, the bulk of the computation in TCMF can be distributed. On the technical side, we formulate the problem as a constrained nonconvex nonsmooth optimization problem. Despite the intricate nature of the problem, we provide a Taylor series characterization of its solution by solving the corresponding Karush-Kuhn-Tucker conditions. Using this characterization, we can show that the alternating minimization algorithm makes significant progress at each iteration and converges into the ground truth at a linear rate. Numerical experiments in video segmentation and anomaly detection highlight the superior feature extraction abilities of TCMF. | [
"['Naichen Shi' 'Salar Fattahi' 'Raed Al Kontar']"
]
|
null | null | 2404.07956 | null | null | http://arxiv.org/pdf/2404.07956v2 | 2024-06-05T00:30:57Z | 2024-04-11T17:49:15Z | Lyapunov-stable Neural Control for State and Output Feedback: A Novel
Formulation | Learning-based neural network (NN) control policies have shown impressive empirical performance in a wide range of tasks in robotics and control. However, formal (Lyapunov) stability guarantees over the region-of-attraction (ROA) for NN controllers with nonlinear dynamical systems are challenging to obtain, and most existing approaches rely on expensive solvers such as sums-of-squares (SOS), mixed-integer programming (MIP), or satisfiability modulo theories (SMT). In this paper, we demonstrate a new framework for learning NN controllers together with Lyapunov certificates using fast empirical falsification and strategic regularizations. We propose a novel formulation that defines a larger verifiable region-of-attraction (ROA) than shown in the literature, and refines the conventional restrictive constraints on Lyapunov derivatives to focus only on certifiable ROAs. The Lyapunov condition is rigorously verified post-hoc using branch-and-bound with scalable linear bound propagation-based NN verification techniques. The approach is efficient and flexible, and the full training and verification procedure is accelerated on GPUs without relying on expensive solvers for SOS, MIP, nor SMT. The flexibility and efficiency of our framework allow us to demonstrate Lyapunov-stable output feedback control with synthesized NN-based controllers and NN-based observers with formal stability guarantees, for the first time in literature. Source code at https://github.com/Verified-Intelligence/Lyapunov_Stable_NN_Controllers | [
"['Lujie Yang' 'Hongkai Dai' 'Zhouxing Shi' 'Cho-Jui Hsieh' 'Russ Tedrake'\n 'Huan Zhang']"
]
|
null | null | 2404.07962 | null | null | http://arxiv.org/pdf/2404.07962v1 | 2024-03-23T02:48:53Z | 2024-03-23T02:48:53Z | Live and Learn: Continual Action Clustering with Incremental Views | Multi-view action clustering leverages the complementary information from different camera views to enhance the clustering performance. Although existing approaches have achieved significant progress, they assume all camera views are available in advance, which is impractical when the camera view is incremental over time. Besides, learning the invariant information among multiple camera views is still a challenging issue, especially in continual learning scenario. Aiming at these problems, we propose a novel continual action clustering (CAC) method, which is capable of learning action categories in a continual learning manner. To be specific, we first devise a category memory library, which captures and stores the learned categories from historical views. Then, as a new camera view arrives, we only need to maintain a consensus partition matrix, which can be updated by leveraging the incoming new camera view rather than keeping all of them. Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized. The empirical experimental results on 6 realistic multi-view action collections demonstrate the excellent clustering performance and time/space efficiency of the CAC compared with 15 state-of-the-art baselines. | [
"['Xiaoqiang Yan' 'Yingtao Gan' 'Yiqiao Mao' 'Yangdong Ye' 'Hui Yu']"
]
|
null | null | 2404.07963 | null | null | http://arxiv.org/pdf/2404.07963v1 | 2024-03-23T18:19:17Z | 2024-03-23T18:19:17Z | EduAgent: Generative Student Agents in Learning | Student simulation in online education is important to address dynamic learning behaviors of students with diverse backgrounds. Existing simulation models based on deep learning usually need massive training data, lacking prior knowledge in educational contexts. Large language models (LLMs) may contain such prior knowledge since they are pre-trained from a large corpus. However, because student behaviors are dynamic and multifaceted with individual differences, directly prompting LLMs is not robust nor accurate enough to capture fine-grained interactions among diverse student personas, learning behaviors, and learning outcomes. This work tackles this problem by presenting a newly annotated fine-grained large-scale dataset and proposing EduAgent, a novel generative agent framework incorporating cognitive prior knowledge (i.e., theoretical findings revealed in cognitive science) to guide LLMs to first reason correlations among various behaviors and then make simulations. Our two experiments show that EduAgent could not only mimic and predict learning behaviors of real students but also generate realistic learning behaviors of virtual students without real data. | [
"['Songlin Xu' 'Xinyu Zhang' 'Lianhui Qin']"
]
|
null | null | 2404.07966 | null | null | http://arxiv.org/pdf/2404.07966v2 | 2024-04-12T03:46:38Z | 2024-03-24T19:32:23Z | Machine Learning-based Approach for Ex-post Assessment of Community Risk
and Resilience Based on Coupled Human-infrastructure Systems Performance | There is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience. | [
"['Xiangpeng Li' 'Ali Mostafavi']"
]
|
null | null | 2404.07968 | null | null | http://arxiv.org/pdf/2404.07968v1 | 2024-03-25T08:40:58Z | 2024-03-25T08:40:58Z | AD-NEv++ : The multi-architecture neuroevolution-based multivariate
anomaly detection framework | Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. However, existing frameworks incorporating neuroevolution lack of support for new layers and architectures and are typically limited to convolutional and LSTM layers. In this paper we propose AD-NEv++, a three-stage neuroevolution-based method that synergically combines subspace evolution, model evolution, and fine-tuning. Our method overcomes the limitations of existing approaches by optimizing the mutation operator in the neuroevolution process, while supporting a wide spectrum of neural layers, including attention, dense, and graph convolutional layers. Our extensive experimental evaluation was conducted with widely adopted multivariate anomaly detection benchmark datasets, and showed that the models generated by AD-NEv++ outperform well-known deep learning architectures and neuroevolution-based approaches for anomaly detection. Moreover, results show that AD-NEv++ can improve and outperform the state-of-the-art GNN (Graph Neural Networks) model architecture in all anomaly detection benchmarks. | [
"['Marcin Pietroń' 'Dominik Żurek' 'Kamil Faber' 'Roberto Corizzo']"
]
|
null | null | 2404.07969 | null | null | http://arxiv.org/pdf/2404.07969v1 | 2024-03-25T15:23:22Z | 2024-03-25T15:23:22Z | An End-to-End Structure with Novel Position Mechanism and Improved EMD
for Stock Forecasting | As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, existing research mostly focuses on individual stock information but ignores stock market information and high noise in stock data. In this paper, we propose a novel method using the attention mechanism in which both stock market information and individual stock information are considered. Meanwhile, we propose a novel EMD-based algorithm for reducing short-term noise in stock data. Two randomly selected exchange-traded funds (ETFs) spanning over ten years from US stock markets are used to demonstrate the superior performance of the proposed attention-based method. The experimental analysis demonstrates that the proposed attention-based method significantly outperforms other state-of-the-art baselines. Code is available at https://github.com/DurandalLee/ACEFormer. | [
"['Chufeng Li' 'Jianyong Chen']"
]
|
null | null | 2404.07970 | null | null | http://arxiv.org/pdf/2404.07970v3 | 2024-06-18T21:37:25Z | 2024-04-11T17:55:05Z | Differentiable All-pole Filters for Time-varying Audio Systems | Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-to-end training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been proposed and widely used in previous works, they cannot accurately reflect the gradient of the original system. We alleviate this difficulty by re-expressing a time-varying all-pole filter to backpropagate the gradients through itself, so the filter implementation is not bound to the technical limitations of automatic differentiation frameworks. This implementation can be employed within audio systems containing filters with poles for efficient gradient evaluation. We demonstrate its training efficiency and expressive capabilities for modelling real-world dynamic audio systems on a phaser, time-varying subtractive synthesiser, and feed-forward compressor. We make our code and audio samples available and provide the trained audio effect and synth models in a VST plugin at https://diffapf.github.io/web/. | [
"['Chin-Yun Yu' 'Christopher Mitcheltree' 'Alistair Carson' 'Stefan Bilbao'\n 'Joshua D. Reiss' 'György Fazekas']"
]
|
null | null | 2404.07979 | null | null | http://arxiv.org/pdf/2404.07979v1 | 2024-04-11T17:57:22Z | 2024-04-11T17:57:22Z | LLoCO: Learning Long Contexts Offline | Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-efficient finetuning. Our method enables an LLM to create a concise representation of the original context and efficiently retrieve relevant information to answer questions accurately. We introduce LLoCO, a technique that combines context compression, retrieval, and parameter-efficient finetuning using LoRA. Our approach extends the effective context window of a 4k token LLaMA2-7B model to handle up to 128k tokens. We evaluate our approach on several long-context question-answering datasets, demonstrating that LLoCO significantly outperforms in-context learning while using $30times$ fewer tokens during inference. LLoCO achieves up to $7.62times$ speed-up and substantially reduces the cost of long document question answering, making it a promising solution for efficient long context processing. Our code is publicly available at https://github.com/jeffreysijuntan/lloco. | [
"['Sijun Tan' 'Xiuyu Li' 'Shishir Patil' 'Ziyang Wu' 'Tianjun Zhang'\n 'Kurt Keutzer' 'Joseph E. Gonzalez' 'Raluca Ada Popa']"
]
|
null | null | 2404.07982 | null | null | http://arxiv.org/pdf/2404.07982v3 | 2024-05-13T13:30:35Z | 2024-04-11T17:58:05Z | Language Imbalance Can Boost Cross-lingual Generalisation | Multilinguality is crucial for extending recent advancements in language modelling to diverse linguistic communities. To maintain high performance while representing multiple languages, multilingual models ideally align representations, allowing what is learned in one language to generalise to others. Prior research has emphasised the importance of parallel data and shared vocabulary elements as key factors for such alignment. In this study, we investigate an unintuitive novel driver of cross-lingual generalisation: language imbalance. In controlled experiments on perfectly equivalent cloned languages, we observe that the existence of a predominant language during training boosts the performance of less frequent languages and leads to stronger alignment of model representations across languages. Furthermore, we find that this trend is amplified with scale: with large enough models or long enough training, we observe that bilingual training data with a 90/10 language split yields better performance on both languages than a balanced 50/50 split. Building on these insights, we design training schemes that can improve performance in all cloned languages, even without altering the training data. As we extend our analysis to real languages, we find that infrequent languages still benefit from frequent ones, yet whether language imbalance causes cross-lingual generalisation there is not conclusive. | [
"['Anton Schäfer' 'Shauli Ravfogel' 'Thomas Hofmann' 'Tiago Pimentel'\n 'Imanol Schlag']"
]
|
null | null | 2404.07983 | null | null | http://arxiv.org/pdf/2404.07983v1 | 2024-04-11T17:58:06Z | 2024-04-11T17:58:06Z | Two Effects, One Trigger: On the Modality Gap, Object Bias, and
Information Imbalance in Contrastive Vision-Language Representation Learning | Contrastive vision-language models like CLIP have gained popularity for their versatile applicable learned representations in various downstream tasks. Despite their successes in some tasks, like zero-shot image recognition, they also perform surprisingly poor on other tasks, like attribute detection. Previous work has attributed these challenges to the modality gap, a separation of image and text in the shared representation space, and a bias towards objects over other factors, such as attributes. In this work we investigate both phenomena. We find that only a few embedding dimensions drive the modality gap. Further, we propose a measure for object bias and find that object bias does not lead to worse performance on other concepts, such as attributes. But what leads to the emergence of the modality gap and object bias? To answer this question we carefully designed an experimental setting which allows us to control the amount of shared information between the modalities. This revealed that the driving factor behind both, the modality gap and the object bias, is the information imbalance between images and captions. | [
"['Simon Schrodi' 'David T. Hoffmann' 'Max Argus' 'Volker Fischer'\n 'Thomas Brox']"
]
|
null | null | 2404.07987 | null | null | http://arxiv.org/pdf/2404.07987v1 | 2024-04-11T17:59:09Z | 2024-04-11T17:59:09Z | ControlNet++: Improving Conditional Controls with Efficient Consistency
Feedback | To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency between generated images and conditional controls. Specifically, for an input conditional control, we use a pre-trained discriminative reward model to extract the corresponding condition of the generated images, and then optimize the consistency loss between the input conditional control and extracted condition. A straightforward implementation would be generating images from random noises and then calculating the consistency loss, but such an approach requires storing gradients for multiple sampling timesteps, leading to considerable time and memory costs. To address this, we introduce an efficient reward strategy that deliberately disturbs the input images by adding noise, and then uses the single-step denoised images for reward fine-tuning. This avoids the extensive costs associated with image sampling, allowing for more efficient reward fine-tuning. Extensive experiments show that ControlNet++ significantly improves controllability under various conditional controls. For example, it achieves improvements over ControlNet by 7.9% mIoU, 13.4% SSIM, and 7.6% RMSE, respectively, for segmentation mask, line-art edge, and depth conditions. | [
"['Ming Li' 'Taojiannan Yang' 'Huafeng Kuang' 'Jie Wu' 'Zhaoning Wang'\n 'Xuefeng Xiao' 'Chen Chen']"
]
|
null | null | 2404.07989 | null | null | http://arxiv.org/pdf/2404.07989v2 | 2024-05-31T01:36:53Z | 2024-04-11T17:59:45Z | Any2Point: Empowering Any-modality Large Models for Efficient 3D
Understanding | Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains. However, such 2D-to-3D approaches are still limited, due to the potential loss of spatial geometries and high computation cost. More importantly, their frameworks are mainly designed for 2D models, lacking a general any-to-3D paradigm. In this paper, we introduce Any2Point, a parameter-efficient method to empower any-modality large models (vision, language, audio) for 3D understanding. Given a frozen transformer from any source modality, we propose a 3D-to-any (1D or 2D) virtual projection strategy that correlates the input 3D points to the original 1D or 2D positions within the source modality. This mechanism enables us to assign each 3D token with a positional encoding paired with the pre-trained model, which avoids 3D geometry loss caused by the true projection and better motivates the transformer for 3D learning with 1D/2D positional priors. Then, within each transformer block, we insert an any-to-3D guided adapter module for parameter-efficient fine-tuning. The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers. We conduct extensive experiments to showcase the effectiveness and efficiency of our method. Code and models are released at https://github.com/Ivan-Tang-3D/Any2Point. | [
"['Yiwen Tang' 'Ray Zhang' 'Jiaming Liu' 'Zoey Guo' 'Dong Wang'\n 'Zhigang Wang' 'Bin Zhao' 'Shanghang Zhang' 'Peng Gao' 'Hongsheng Li'\n 'Xuelong Li']"
]
|
null | null | 2404.07999 | null | null | http://arxiv.org/pdf/2404.07999v1 | 2024-04-07T03:04:34Z | 2024-04-07T03:04:34Z | A Multi-Level Framework for Accelerating Training Transformer Models | The fast growing capabilities of large-scale deep learning models, such as Bert, GPT and ViT, are revolutionizing the landscape of NLP, CV and many other domains. Training such models, however, poses an unprecedented demand for computing power, which incurs exponentially increasing energy cost and carbon dioxide emissions. It is thus critical to develop efficient training solutions to reduce the training costs. Motivated by a set of key observations of inter- and intra-layer similarities among feature maps and attentions that can be identified from typical training processes, we propose a multi-level framework for training acceleration. Specifically, the framework is based on three basic operators, Coalescing, De-coalescing and Interpolation, which can be orchestrated to build a multi-level training framework. The framework consists of a V-cycle training process, which progressively down- and up-scales the model size and projects the parameters between adjacent levels of models via coalescing and de-coalescing. The key idea is that a smaller model that can be trained for fast convergence and the trained parameters provides high-qualities intermediate solutions for the next level larger network. The interpolation operator is designed to break the symmetry of neurons incurred by de-coalescing for better convergence performance. Our experiments on transformer-based language models (e.g. Bert, GPT) as well as a vision model (e.g. DeiT) prove that the proposed framework reduces the computational cost by about 20% on training BERT/GPT-Base models and up to 51.6% on training the BERT-Large model while preserving the performance. | [
"['Longwei Zou' 'Han Zhang' 'Yangdong Deng']"
]
|
null | null | 2404.08001 | null | null | http://arxiv.org/pdf/2404.08001v1 | 2024-04-08T07:37:31Z | 2024-04-08T07:37:31Z | Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics | Large Language Models (LLMs) are undergoing a period of rapid updates and changes, with state-of-the-art (SOTA) model frequently being replaced. When applying LLMs to a specific scientific field, it's challenging to acquire unique domain knowledge while keeping the model itself advanced. To address this challenge, a sophisticated large language model system named as Xiwu has been developed, allowing you switch between the most advanced foundation models and quickly teach the model domain knowledge. In this work, we will report on the best practices for applying LLMs in the field of high-energy physics (HEP), including: a seed fission technology is proposed and some data collection and cleaning tools are developed to quickly obtain domain AI-Ready dataset; a just-in-time learning system is implemented based on the vector store technology; an on-the-fly fine-tuning system has been developed to facilitate rapid training under a specified foundation model. The results show that Xiwu can smoothly switch between foundation models such as LLaMA, Vicuna, ChatGLM and Grok-1. The trained Xiwu model is significantly outperformed the benchmark model on the HEP knowledge question-and-answering and code generation. This strategy significantly enhances the potential for growth of our model's performance, with the hope of surpassing GPT-4 as it evolves with the development of open-source models. This work provides a customized LLM for the field of HEP, while also offering references for applying LLM to other fields, the corresponding codes are available on Github. | [
"['Zhengde Zhang' 'Yiyu Zhang' 'Haodong Yao' 'Jianwen Luo' 'Rui Zhao'\n 'Bo Huang' 'Jiameng Zhao' 'Yipu Liao' 'Ke Li' 'Lina Zhao' 'Jun Cao'\n 'Fazhi Qi' 'Changzheng Yuan']"
]
|
null | null | 2404.08002 | null | null | http://arxiv.org/pdf/2404.08002v1 | 2024-04-08T09:54:57Z | 2024-04-08T09:54:57Z | ApproxDARTS: Differentiable Neural Architecture Search with Approximate
Multipliers | Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy. In this work, we present ApproxDARTS, a neural architecture search (NAS) method enabling the popular differentiable neural architecture search method called DARTS to exploit approximate multipliers and thus reduce the power consumption of generated neural networks. We showed on the CIFAR-10 data set that the ApproxDARTS is able to perform a complete architecture search within less than $10$ GPU hours and produce competitive convolutional neural networks (CNN) containing approximate multipliers in convolutional layers. For example, ApproxDARTS created a CNN showing an energy consumption reduction of (a) $53.84%$ in the arithmetic operations of the inference phase compared to the CNN utilizing the native $32$-bit floating-point multipliers and (b) $5.97%$ compared to the CNN utilizing the exact $8$-bit fixed-point multipliers, in both cases with a negligible accuracy drop. Moreover, the ApproxDARTS is $2.3times$ faster than a similar but evolutionary algorithm-based method called EvoApproxNAS. | [
"['Michal Pinos' 'Lukas Sekanina' 'Vojtech Mrazek']"
]
|
null | null | 2404.08003 | null | null | http://arxiv.org/pdf/2404.08003v2 | 2024-04-15T00:59:59Z | 2024-04-09T04:21:13Z | Asynchronous Federated Reinforcement Learning with Policy Gradient
Updates: Algorithm Design and Convergence Analysis | To improve the efficiency of reinforcement learning, we propose a novel asynchronous federated reinforcement learning framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (PG) updates. To handle the challenge of lagged policies in asynchronous settings, we design delay-adaptive lookahead and normalized update techniques that can effectively handle the heterogeneous arrival times of policy gradients. We analyze the theoretical global convergence bound of AFedPG, and characterize the advantage of the proposed algorithm in terms of both the sample complexity and time complexity. Specifically, our AFedPG method achieves $mathcal{O}(frac{{epsilon}^{-2.5}}{N})$ sample complexity at each agent on average. Compared to the single agent setting with $mathcal{O}(epsilon^{-2.5})$ sample complexity, it enjoys a linear speedup with respect to the number of agents. Moreover, compared to synchronous FedPG, AFedPG improves the time complexity from $mathcal{O}(frac{t_{max}}{N})$ to $mathcal{O}(frac{1}{sum_{i=1}^{N} frac{1}{t_{i}}})$, where $t_{i}$ denotes the time consumption in each iteration at the agent $i$, and $t_{max}$ is the largest one. The latter complexity $mathcal{O}(frac{1}{sum_{i=1}^{N} frac{1}{t_{i}}})$ is always smaller than the former one, and this improvement becomes significant in large-scale federated settings with heterogeneous computing powers ($t_{max}gg t_{min}$). Finally, we empirically verify the improved performances of AFedPG in three MuJoCo environments with varying numbers of agents. We also demonstrate the improvements with different computing heterogeneity. | [
"['Guangchen Lan' 'Dong-Jun Han' 'Abolfazl Hashemi' 'Vaneet Aggarwal'\n 'Christopher G. Brinton']"
]
|
null | null | 2404.08004 | null | null | http://arxiv.org/pdf/2404.08004v1 | 2024-04-09T05:51:40Z | 2024-04-09T05:51:40Z | GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle
Trajectory Prediction | As a vital component in autonomous driving, accurate trajectory prediction effectively prevents traffic accidents and improves driving efficiency. To capture complex spatial-temporal dynamics and social interactions, recent studies developed models based on advanced deep-learning methods. On the other hand, recent studies have explored the use of deep generative models to further account for trajectory uncertainties. However, the current approaches demonstrating indeterminacy involve inefficient and time-consuming practices such as sampling from trained models. To fill this gap, we proposed a novel model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle trajectory prediction while efficiently quantifying prediction uncertainty. In particular, GRANP contains an encoder with deterministic and latent paths, and a decoder for prediction. The encoder, including stacked Graph Attention Networks, LSTM and 1D convolutional layers, is employed to extract spatial-temporal relationships. The decoder is used to learn a latent distribution and thus quantify prediction uncertainty. To reveal the effectiveness of our model, we evaluate the performance of GRANP on the highD dataset. Extensive experiments show that GRANP achieves state-of-the-art results and can efficiently quantify uncertainties. Additionally, we undertake an intuitive case study that showcases the interpretability of the proposed approach. The code is available at https://github.com/joy-driven/GRANP. | [
"['Yuhao Luo' 'Kehua Chen' 'Meixin Zhu']"
]
|
null | null | 2404.08005 | null | null | http://arxiv.org/abs/2404.08005v2 | 2024-06-18T05:51:50Z | 2024-04-09T06:23:41Z | Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS | One of the primary challenges impeding the progress of Neural Architecture Search (NAS) is its extensive reliance on exorbitant computational resources. NAS benchmarks aim to simulate runs of NAS experiments at zero cost, remediating the need for extensive compute. However, existing NAS benchmarks use synthetic datasets and model proxies that make simplified assumptions about the characteristics of these datasets and models, leading to unrealistic evaluations. We present a technique that allows searching for training proxies that reduce the cost of benchmark construction by significant margins, making it possible to construct realistic NAS benchmarks for large-scale datasets. Using this technique, we construct an open-source bi-objective NAS benchmark for the ImageNet2012 dataset combined with the on-device performance of accelerators, including GPUs, TPUs, and FPGAs. Through extensive experimentation with various NAS optimizers and hardware platforms, we show that the benchmark is accurate and allows searching for state-of-the-art hardware-aware models at zero cost. | [
"['Afzal Ahmad' 'Linfeng Du' 'Zhiyao Xie' 'Wei Zhang']"
]
|
null | null | 2404.08006 | null | null | http://arxiv.org/pdf/2404.08006v1 | 2024-04-09T11:45:16Z | 2024-04-09T11:45:16Z | Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative
Human-Robot Order Picking | In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimization problem in such systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel multi-objective Deep Reinforcement Learning (DRL) approach to learn effective allocation policies to maximize pick efficiency while also aiming to improve workload fairness amongst human pickers. In our approach, we model the warehouse states using a graph, and define a neural network architecture that captures regional information and effectively extracts representations related to efficiency and workload. We develop a discrete-event simulation model, which we use to train and evaluate the proposed DRL approach. In the experiments, we demonstrate that our approach can find non-dominated policy sets that outline good trade-offs between fairness and efficiency objectives. The trained policies outperform the benchmarks in terms of both efficiency and fairness. Moreover, they show good transferability properties when tested on scenarios with different warehouse sizes. The implementation of the simulation model, proposed approach, and experiments are published. | [
"['Igor G. Smit' 'Zaharah Bukhsh' 'Mykola Pechenizkiy'\n 'Kostas Alogariastos' 'Kasper Hendriks' 'Yingqian Zhang']"
]
|
null | null | 2404.08007 | null | null | http://arxiv.org/pdf/2404.08007v1 | 2024-04-09T12:37:41Z | 2024-04-09T12:37:41Z | Interpretable Neural Temporal Point Processes for Modelling Electronic
Health Records | Electronic Health Records (EHR) can be represented as temporal sequences that record the events (medical visits) from patients. Neural temporal point process (NTPP) has achieved great success in modeling event sequences that occur in continuous time space. However, due to the black-box nature of neural networks, existing NTPP models fall short in explaining the dependencies between different event types. In this paper, inspired by word2vec and Hawkes process, we propose an interpretable framework inf2vec for event sequence modelling, where the event influences are directly parameterized and can be learned end-to-end. In the experiment, we demonstrate the superiority of our model on event prediction as well as type-type influences learning. | [
"['Bingqing Liu']"
]
|
null | null | 2404.08008 | null | null | http://arxiv.org/pdf/2404.08008v1 | 2024-04-10T01:26:24Z | 2024-04-10T01:26:24Z | Sample-Efficient Human Evaluation of Large Language Models via Maximum
Discrepancy Competition | The past years have witnessed a proliferation of large language models (LLMs). Yet, automated and unbiased evaluation of LLMs is challenging due to the inaccuracy of standard metrics in reflecting human preferences and the inefficiency in sampling informative and diverse test examples. While human evaluation remains the gold standard, it is expensive and time-consuming, especially when dealing with a large number of testing samples. To address this problem, we propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs, whose responses are subject to three-alternative forced choice by human subjects. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system. We select eight representative LLMs and compare them in terms of four skills: knowledge understanding, mathematical reasoning, writing, and coding. Experimental results show that the proposed method achieves a reliable and sensible ranking of LLMs' capabilities, identifies their relative strengths and weaknesses, and offers valuable insights for further LLM advancement. | [
"['Kehua Feng' 'Keyan Ding' 'Kede Ma' 'Zhihua Wang' 'Qiang Zhang'\n 'Huajun Chen']"
]
|
null | null | 2404.08010 | null | null | http://arxiv.org/pdf/2404.08010v2 | 2024-04-15T06:08:51Z | 2024-04-10T03:22:58Z | Differentiable Search for Finding Optimal Quantization Strategy | To accelerate and compress deep neural networks (DNNs), many network quantization algorithms have been proposed. Although the quantization strategy of any algorithm from the state-of-the-arts may outperform others in some network architectures, it is hard to prove the strategy is always better than others, and even cannot judge that the strategy is always the best choice for all layers in a network. In other words, existing quantization algorithms are suboptimal as they ignore the different characteristics of different layers and quantize all layers by a uniform quantization strategy. To solve the issue, in this paper, we propose a differentiable quantization strategy search (DQSS) to assign optimal quantization strategy for individual layer by taking advantages of the benefits of different quantization algorithms. Specifically, we formulate DQSS as a differentiable neural architecture search problem and adopt an efficient convolution to efficiently explore the mixed quantization strategies from a global perspective by gradient-based optimization. We conduct DQSS for post-training quantization to enable their performance to be comparable with that in full precision models. We also employ DQSS in quantization-aware training for further validating the effectiveness of DQSS. To circumvent the expensive optimization cost when employing DQSS in quantization-aware training, we update the hyper-parameters and the network parameters in a single forward-backward pass. Besides, we adjust the optimization process to avoid the potential under-fitting problem. Comprehensive experiments on high level computer vision task, i.e., image classification, and low level computer vision task, i.e., image super-resolution, with various network architectures show that DQSS could outperform the state-of-the-arts. | [
"['Lianqiang Li' 'Chenqian Yan' 'Yefei Chen']"
]
|
null | null | 2404.08011 | null | null | http://arxiv.org/pdf/2404.08011v1 | 2024-04-10T06:30:33Z | 2024-04-10T06:30:33Z | An inclusive review on deep learning techniques and their scope in
handwriting recognition | Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. For the achievement of state-of-the-art performances in diverse domains, the deep learning used different architectures and these architectures used activation functions to perform various computations between hidden and output layers of any architecture. This paper presents a survey on the existing studies of deep learning in handwriting recognition field. Even though the recent progress indicates that the deep learning methods has provided valuable means for speeding up or proving accurate results in handwriting recognition, but following from the extensive literature survey, the present study finds that the deep learning has yet to revolutionize more and has to resolve many of the most pressing challenges in this field, but promising advances have been made on the prior state of the art. Additionally, an inadequate availability of labelled data to train presents problems in this domain. Nevertheless, the present handwriting recognition survey foresees deep learning enabling changes at both bench and bedside with the potential to transform several domains as image processing, speech recognition, computer vision, machine translation, robotics and control, medical imaging, medical information processing, bio-informatics, natural language processing, cyber security, and many others. | [
"['Sukhdeep Singh' 'Sudhir Rohilla' 'Anuj Sharma']"
]
|
null | null | 2404.08013 | null | null | http://arxiv.org/pdf/2404.08013v1 | 2024-04-10T15:37:15Z | 2024-04-10T15:37:15Z | Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect
Communication | Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low illumination, winding roads, and crowded traffic. To cover the limitations of former methods, in this paper, we propose a novel approach to realize an optimized CP under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range and enhance the Object Detection (OD) accuracy of the ego vehicle. In this two-step process, we first select the helper vehicles that contribute the most to CP based on their visual range and lowest motion blur. Next, we implement a radio block optimization among the candidate vehicles to further improve communication efficiency. We specifically focus on pedestrian detection as an exemplary scenario. To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception in challenging scenarios, substantially improving driving safety under adverse conditions. Finally, we note that the networking assumptions are adopted from LTE Release 14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V) communication. Nonetheless, our method is flexible and applicable to arbitrary V2V communications. | [
"['Ahmad Sarlak' 'Hazim Alzorgan' 'Sayed Pedram Haeri Boroujeni'\n 'Abolfazl Razi' 'Rahul Amin']"
]
|
null | null | 2404.08016 | null | null | http://arxiv.org/pdf/2404.08016v1 | 2024-04-10T18:36:25Z | 2024-04-10T18:36:25Z | ONNXPruner: ONNX-Based General Model Pruning Adapter | Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various models and platforms remains a significant challenge. To address this challenge, we propose ONNXPruner, a versatile pruning adapter designed for the ONNX format models. ONNXPruner streamlines the adaptation process across diverse deep learning frameworks and hardware platforms. A novel aspect of ONNXPruner is its use of node association trees, which automatically adapt to various model architectures. These trees clarify the structural relationships between nodes, guiding the pruning process, particularly highlighting the impact on interconnected nodes. Furthermore, we introduce a tree-level evaluation method. By leveraging node association trees, this method allows for a comprehensive analysis beyond traditional single-node evaluations, enhancing pruning performance without the need for extra operations. Experiments across multiple models and datasets confirm ONNXPruner's strong adaptability and increased efficacy. Our work aims to advance the practical application of model pruning. | [
"['Dongdong Ren' 'Wenbin Li' 'Tianyu Ding' 'Lei Wang' 'Qi Fan' 'Jing Huo'\n 'Hongbing Pan' 'Yang Gao']"
]
|
null | null | 2404.08019 | null | null | http://arxiv.org/pdf/2404.08019v1 | 2024-04-11T01:30:05Z | 2024-04-11T01:30:05Z | Learning Chemotherapy Drug Action via Universal Physics-Informed Neural
Networks | Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics. We learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and E_max) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics. As these are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models. | [
"['Lena Podina' 'Ali Ghodsi' 'Mohammad Kohandel']"
]
|
null | null | 2404.08020 | null | null | http://arxiv.org/abs/2404.08020v1 | 2024-04-11T05:53:38Z | 2024-04-11T05:53:38Z | Augmenting Knowledge Graph Hierarchies Using Neural Transformers | Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclical generation. We present techniques for augmenting hierarchies, which led to coverage increase by 98% for intents and 99% for colors in our knowledge graph. | [
"['Sanat Sharma' 'Mayank Poddar' 'Jayant Kumar' 'Kosta Blank' 'Tracy King']"
]
|
null | null | 2404.08021 | null | null | http://arxiv.org/pdf/2404.08021v1 | 2024-04-11T06:19:55Z | 2024-04-11T06:19:55Z | VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and
Representation Learning | Trajectory similarity search plays an essential role in autonomous driving, as it enables vehicles to analyze the information and characteristics of different trajectories to make informed decisions and navigate safely in dynamic environments. Existing work on the trajectory similarity search task primarily utilizes sequence-processing algorithms or Recurrent Neural Networks (RNNs), which suffer from the inevitable issues of complicated architecture and heavy training costs. Considering the intricate connections between trajectories, using Graph Neural Networks (GNNs) for data modeling is feasible. However, most methods directly use existing mathematical graph structures as the input instead of constructing specific graphs from certain vehicle trajectory data. This ignores such data's unique and dynamic characteristics. To bridge such a research gap, we propose VeTraSS -- an end-to-end pipeline for Vehicle Trajectory Similarity Search. Specifically, VeTraSS models the original trajectory data into multi-scale graphs, and generates comprehensive embeddings through a novel multi-layer attention-based GNN. The learned embeddings can be used for searching similar vehicle trajectories. Extensive experiments on the Porto and Geolife datasets demonstrate the effectiveness of VeTraSS, where our model outperforms existing work and reaches the state-of-the-art. This demonstrates the potential of VeTraSS for trajectory analysis and safe navigation in self-driving vehicles in the real world. | [
"['Ming Cheng' 'Bowen Zhang' 'Ziyu Wang' 'Ziyi Zhou' 'Weiqi Feng' 'Yi Lyu'\n 'Xingjian Diao']"
]
|
null | null | 2404.08023 | null | null | http://arxiv.org/pdf/2404.08023v1 | 2024-04-11T09:07:40Z | 2024-04-11T09:07:40Z | Pathology-genomic fusion via biologically informed cross-modality graph
learning for survival analysis | The diagnosis and prognosis of cancer are typically based on multi-modal clinical data, including histology images and genomic data, due to the complex pathogenesis and high heterogeneity. Despite the advancements in digital pathology and high-throughput genome sequencing, establishing effective multi-modal fusion models for survival prediction and revealing the potential association between histopathology and transcriptomics remains challenging. In this paper, we propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. The representation learning network utilizes the biological prior knowledge of intra-modal and inter-modal data associations to guide the feature extraction. The node features of each modality are updated through attention-based graph learning strategy. Unimodal features and bi-modal fused features are extracted via attention pooling module and then used for survival prediction. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas (TCGA) and the First Affiliated Hospital of Zhengzhou University (FAHZU). Extensive experimental results demonstrate that the proposed method outperforms both unimodal and other multi-modal fusion models. For demonstrating the model interpretability, we also visualize the attention heatmap of pathological images and utilize integrated gradient algorithm to identify important tissue structure, biological pathways and key genes. | [
"['Zeyu Zhang' 'Yuanshen Zhao' 'Jingxian Duan' 'Yaou Liu' 'Hairong Zheng'\n 'Dong Liang' 'Zhenyu Zhang' 'Zhi-Cheng Li']"
]
|
null | null | 2404.08024 | null | null | http://arxiv.org/pdf/2404.08024v1 | 2024-04-11T09:52:39Z | 2024-04-11T09:52:39Z | The OxMat dataset: a multimodal resource for the development of
AI-driven technologies in maternal and newborn child health | The rapid advancement of Artificial Intelligence (AI) in healthcare presents a unique opportunity for advancements in obstetric care, particularly through the analysis of cardiotocography (CTG) for fetal monitoring. However, the effectiveness of such technologies depends upon the availability of large, high-quality datasets that are suitable for machine learning. This paper introduces the Oxford Maternity (OxMat) dataset, the world's largest curated dataset of CTGs, featuring raw time series CTG data and extensive clinical data for both mothers and babies, which is ideally placed for machine learning. The OxMat dataset addresses the critical gap in women's health data by providing over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated and reviewed since 1991. The dataset also comprises over 200 antepartum, intrapartum and postpartum clinical variables, ensuring near-complete data for crucial outcomes such as stillbirth and acidaemia. While this dataset also covers the intrapartum stage, around 94% of the constituent CTGS are antepartum. This allows for a unique focus on the underserved antepartum period, in which early detection of at-risk fetuses can significantly improve health outcomes. Our comprehensive review of existing datasets reveals the limitations of current datasets: primarily, their lack of sufficient volume, detailed clinical data and antepartum data. The OxMat dataset lays a foundation for future AI-driven prenatal care, offering a robust resource for developing and testing algorithms aimed at improving maternal and fetal health outcomes. | [
"['M. Jaleed Khan' 'Ioana Duta' 'Beth Albert' 'William Cooke' 'Manu Vatish'\n 'Gabriel Davis Jones']"
]
|
null | null | 2404.08027 | null | null | http://arxiv.org/pdf/2404.08027v1 | 2024-04-11T15:58:12Z | 2024-04-11T15:58:12Z | SurvMamba: State Space Model with Multi-grained Multi-modal Interaction
for Survival Prediction | Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slide images (WSIs) and transcriptomic data, from which better intra-modal representations and inter-modal integration could be derived. Moreover, many existing studies attempt to improve multi-modal representations through attention mechanisms, which inevitably lead to high complexity when processing high-dimensional WSIs and transcriptomic data. Recently, a structured state space model named Mamba emerged as a promising approach for its superior performance in modeling long sequences with low complexity. In this study, we propose Mamba with multi-grained multi-modal interaction (SurvMamba) for survival prediction. SurvMamba is implemented with a Hierarchical Interaction Mamba (HIM) module that facilitates efficient intra-modal interactions at different granularities, thereby capturing more detailed local features as well as rich global representations. In addition, an Interaction Fusion Mamba (IFM) module is used for cascaded inter-modal interactive fusion, yielding more comprehensive features for survival prediction. Comprehensive evaluations on five TCGA datasets demonstrate that SurvMamba outperforms other existing methods in terms of performance and computational cost. | [
"['Ying Chen' 'Jiajing Xie' 'Yuxiang Lin' 'Yuhang Song' 'Wenxian Yang'\n 'Rongshan Yu']"
]
|
null | null | 2404.08028 | null | null | http://arxiv.org/pdf/2404.08028v1 | 2024-04-11T16:23:28Z | 2024-04-11T16:23:28Z | FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task
Learning for Network Edge Traffic Classification | Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge traffic classification. Despite its recognized advantages, FL encounters obstacles linked to statistical data heterogeneity and labeled data scarcity during the training of single-task models for machine learning-based traffic classification, leading to hindered learning performance. In response to these challenges, adopting a hard-parameter sharing multi-task learning model with auxiliary tasks proves to be a suitable approach. Such a model has the capability to reduce communication and computation costs, navigate statistical complexities inherent in FL contexts, and overcome labeled data scarcity by leveraging knowledge derived from interconnected auxiliary tasks. This paper introduces a new framework for federated auxiliary hard-parameter sharing multi-task learning, namely, FedAuxHMTL. The introduced framework incorporates model parameter exchanges between edge server and base stations, enabling base stations from distributed areas to participate in the FedAuxHMTL process and enhance the learning performance of the main task-network edge traffic classification. Empirical experiments are conducted to validate and demonstrate the FedAuxHMTL's effectiveness in terms of accuracy, total global loss, communication costs, computing time, and energy consumption compared to its counterparts. | [
"['Faisal Ahmed' 'Myungjin Lee' 'Suresh Subramaniam' 'Motoharu Matsuura'\n 'Hiroshi Hasegawa' 'Shih-Chun Lin']"
]
|
null | null | 2404.08029 | null | null | http://arxiv.org/pdf/2404.08029v1 | 2024-04-11T16:58:29Z | 2024-04-11T16:58:29Z | A Multi-Expert Large Language Model Architecture for Verilog Code
Generation | Recently, there has been a surging interest in using large language models (LLMs) for Verilog code generation. However, the existing approaches are limited in terms of the quality of the generated Verilog code. To address such limitations, this paper introduces an innovative multi-expert LLM architecture for Verilog code generation (MEV-LLM). Our architecture uniquely integrates multiple LLMs, each specifically fine-tuned with a dataset that is categorized with respect to a distinct level of design complexity. It allows more targeted learning, directly addressing the nuances of generating Verilog code for each category. Empirical evidence from experiments highlights notable improvements in terms of the percentage of generated Verilog outputs that are syntactically and functionally correct. These findings underscore the efficacy of our approach, promising a forward leap in the field of automated hardware design through machine learning. | [
"['Bardia Nadimi' 'Hao Zheng']"
]
|
null | null | 2404.08031 | null | null | http://arxiv.org/pdf/2404.08031v1 | 2024-04-11T17:59:52Z | 2024-04-11T17:59:52Z | Latent Guard: a Safety Framework for Text-to-image Generation | With the ability to generate high-quality images, text-to-image (T2I) models can be exploited for creating inappropriate content. To prevent misuse, existing safety measures are either based on text blacklists, which can be easily circumvented, or harmful content classification, requiring large datasets for training and offering low flexibility. Hence, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation. Inspired by blacklist-based approaches, Latent Guard learns a latent space on top of the T2I model's text encoder, where it is possible to check the presence of harmful concepts in the input text embeddings. Our proposed framework is composed of a data generation pipeline specific to the task using large language models, ad-hoc architectural components, and a contrastive learning strategy to benefit from the generated data. The effectiveness of our method is verified on three datasets and against four baselines. Code and data will be shared at https://github.com/rt219/LatentGuard. | [
"['Runtao Liu' 'Ashkan Khakzar' 'Jindong Gu' 'Qifeng Chen' 'Philip Torr'\n 'Fabio Pizzati']"
]
|
null | null | 2404.08061 | null | null | http://arxiv.org/pdf/2404.08061v1 | 2024-04-11T18:03:59Z | 2024-04-11T18:03:59Z | Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial
Internet of Things | The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. Soft (virtual) sensing leverages mathematical models to estimate variables from physical sensor data, offering a solution to these challenges. Data-driven and physics-based modeling are the two main methodologies widely used for soft sensing. The choice between these strategies depends on the complexity of the underlying system, with the data-driven approach often being preferred when the physics-based inference models are intricate and present challenges for state estimation. However, conventional deep learning models are typically hindered by their inability to explicitly represent the complex interactions among various sensors. To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements. In this research, we propose physics-enhanced GNNs, which integrate principles of physics into graph-based methodologies. This is achieved by augmenting additional nodes in the input graph derived from the underlying characteristics of the physical processes. Our evaluation of the proposed methodology on the case study of district heating networks reveals significant improvements over purely data-driven GNNs, even in the presence of noise and parameter inaccuracies. | [
"['Keivan Faghih Niresi' 'Hugo Bissig' 'Henri Baumann' 'Olga Fink']"
]
|
null | null | 2404.08064 | null | null | http://arxiv.org/pdf/2404.08064v2 | 2024-06-22T09:47:05Z | 2024-04-11T18:06:35Z | The Impact of Speech Anonymization on Pathology and Its Limits | Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined. This study investigates anonymization's impact on pathological speech across over 2,700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness. We explore both deep-learning-based and signal processing-based anonymization methods, and document substantial privacy improvements across disorders-evidenced by equal error rate increases up to 1933%, with minimal overall impact on utility. Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate experienced minimal utility changes, while Dysglossia showed slight improvements. Our findings underscore that the impact of anonymization varies substantially across different disorders. This necessitates disorder-specific anonymization strategies to optimally balance privacy with diagnostic utility. Additionally, our fairness analysis revealed consistent anonymization effects across most of the demographics. This study demonstrates the effectiveness of anonymization in pathological speech for enhancing privacy, while also highlighting the importance of customized and disorder-specific approaches to account for inversion attacks. | [
"['Soroosh Tayebi Arasteh' 'Tomas Arias-Vergara' 'Paula Andrea Perez-Toro'\n 'Tobias Weise' 'Kai Packhaeuser' 'Maria Schuster' 'Elmar Noeth'\n 'Andreas Maier' 'Seung Hee Yang']"
]
|
null | null | 2404.08068 | null | null | http://arxiv.org/pdf/2404.08068v1 | 2024-04-11T18:13:21Z | 2024-04-11T18:13:21Z | WildGraph: Realistic Graph-based Trajectory Generation for Wildlife | Trajectory generation is an important task in movement studies; it circumvents the privacy, ethical, and technical challenges of collecting real trajectories from the target population. In particular, real trajectories in the wildlife domain are scarce as a result of ethical and environmental constraints of the collection process. In this paper, we consider the problem of generating long-horizon trajectories, akin to wildlife migration, based on a small set of real samples. We propose a hierarchical approach to learn the global movement characteristics of the real dataset and recursively refine localized regions. Our solution, WildGraph, discretizes the geographic path into a prototype network of H3 (https://www.uber.com/blog/h3/) regions and leverages a recurrent variational auto-encoder to probabilistically generate paths over the regions, based on occupancy. WildGraph successfully generates realistic months-long trajectories using a sample size as small as 60. Experiments performed on two wildlife migration datasets demonstrate that our proposed method improves the generalization of the generated trajectories in comparison to existing work while achieving superior or comparable performance in several benchmark metrics. Our code is published on the following repository: url{https://github.com/aliwister/wildgraph}. | [
"['Ali Al-Lawati' 'Elsayed Eshra' 'Prasenjit Mitra']"
]
|
null | null | 2404.08069 | null | null | http://arxiv.org/pdf/2404.08069v1 | 2024-04-11T18:13:42Z | 2024-04-11T18:13:42Z | Persistent Classification: A New Approach to Stability of Data and
Adversarial Examples | There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high-dimensionality of the data, high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be $(gamma,sigma)$-stable if the probability of the same classification is at least $gamma$ for points sampled in a Gaussian neighborhood of the point with a given standard deviation $sigma$. We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric. | [
"['Brian Bell' 'Michael Geyer' 'David Glickenstein' 'Keaton Hamm'\n 'Carlos Scheidegger' 'Amanda Fernandez' 'Juston Moore']"
]
|
null | null | 2404.08073 | null | null | http://arxiv.org/pdf/2404.08073v1 | 2024-04-11T18:28:01Z | 2024-04-11T18:28:01Z | Spurious Stationarity and Hardness Results for Mirror Descent | Despite the considerable success of Bregman proximal-type algorithms, such as mirror descent, in machine learning, a critical question remains: Can existing stationarity measures, often based on Bregman divergence, reliably distinguish between stationary and non-stationary points? In this paper, we present a groundbreaking finding: All existing stationarity measures necessarily imply the existence of spurious stationary points. We further establish an algorithmic independent hardness result: Bregman proximal-type algorithms are unable to escape from a spurious stationary point in finite steps when the initial point is unfavorable, even for convex problems. Our hardness result points out the inherent distinction between Euclidean and Bregman geometries, and introduces both fundamental theoretical and numerical challenges to both machine learning and optimization communities. | [
"['He Chen' 'Jiajin Li' 'Anthony Man-Cho So']"
]
|
null | null | 2404.08078 | null | null | http://arxiv.org/pdf/2404.08078v1 | 2024-04-11T18:34:11Z | 2024-04-11T18:34:11Z | SQBC: Active Learning using LLM-Generated Synthetic Data for Stance
Detection in Online Political Discussions | Stance detection is an important task for many applications that analyse or support online political discussions. Common approaches include fine-tuning transformer based models. However, these models require a large amount of labelled data, which might not be available. In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model. Second, we propose a new active learning method called SQBC based on the "Query-by-Comittee" approach. The key idea is to use LLM-generated synthetic data as an oracle to identify the most informative unlabelled samples, that are selected for manual labelling. Comprehensive experiments show that both ideas can improve the stance detection performance. Curiously, we observed that fine-tuning on actively selected samples can exceed the performance of using the full dataset. | [
"['Stefan Sylvius Wagner' 'Maike Behrendt' 'Marc Ziegele'\n 'Stefan Harmeling']"
]
|
null | null | 2404.08079 | null | null | http://arxiv.org/pdf/2404.08079v1 | 2024-04-11T18:34:29Z | 2024-04-11T18:34:29Z | DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning
Models | Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However, a pivotal prerequisite for achieving this level of competitiveness is the significant communication and computation overheads when updating these models, which prohibits the applications of them to real-world scenarios. To address this issue, drawing inspiration from advanced model merging techniques without requiring additional training, we introduce the Decentralized Iterative Merging-And-Training (DIMAT) paradigm--a novel decentralized deep learning framework. Within DIMAT, each agent is trained on their local data and periodically merged with their neighboring agents using advanced model merging techniques like activation matching until convergence is achieved. DIMAT provably converges with the best available rate for nonconvex functions with various first-order methods, while yielding tighter error bounds compared to the popular existing approaches. We conduct a comprehensive empirical analysis to validate DIMAT's superiority over baselines across diverse computer vision tasks sourced from multiple datasets. Empirical results validate our theoretical claims by showing that DIMAT attains faster and higher initial gain in accuracy with independent and identically distributed (IID) and non-IID data, incurring lower communication overhead. This DIMAT paradigm presents a new opportunity for the future decentralized learning, enhancing its adaptability to real-world with sparse and light-weight communication and computation. | [
"['Nastaran Saadati' 'Minh Pham' 'Nasla Saleem' 'Joshua R. Waite'\n 'Aditya Balu' 'Zhanhong Jiang' 'Chinmay Hegde' 'Soumik Sarkar']"
]
|
null | null | 2404.08080 | null | null | http://arxiv.org/pdf/2404.08080v1 | 2024-04-11T18:35:49Z | 2024-04-11T18:35:49Z | Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models | Fine-tuning language models (LMs) has demonstrated success in a wide array of downstream tasks. However, as LMs are scaled up, the memory requirements for backpropagation become prohibitively high. Zeroth-order (ZO) optimization methods can leverage memory-efficient forward passes to estimate gradients. More recently, MeZO, an adaptation of ZO-SGD, has been shown to consistently outperform zero-shot and in-context learning when combined with suitable task prompts. In this work, we couple ZO methods with variance reduction techniques to enhance stability and convergence for inference-based LM fine-tuning. We introduce Memory-Efficient Zeroth-Order Stochastic Variance-Reduced Gradient (MeZO-SVRG) and demonstrate its efficacy across multiple LM fine-tuning tasks, eliminating the reliance on task-specific prompts. Evaluated across a range of both masked and autoregressive LMs on benchmark GLUE tasks, MeZO-SVRG outperforms MeZO with up to 20% increase in test accuracies in both full- and partial-parameter fine-tuning settings. MeZO-SVRG benefits from reduced computation time as it often surpasses MeZO's peak test accuracy with a $2times$ reduction in GPU-hours. MeZO-SVRG significantly reduces the required memory footprint compared to first-order SGD, i.e. by $2times$ for autoregressive models. Our experiments highlight that MeZO-SVRG's memory savings progressively improve compared to SGD with larger batch sizes. | [
"['Tanmay Gautam' 'Youngsuk Park' 'Hao Zhou' 'Parameswaran Raman'\n 'Wooseok Ha']"
]
|
null | null | 2404.08089 | null | null | http://arxiv.org/pdf/2404.08089v1 | 2024-04-11T19:07:15Z | 2024-04-11T19:07:15Z | Efficient Duple Perturbation Robustness in Low-rank MDPs | The pursuit of robustness has recently been a popular topic in reinforcement learning (RL) research, yet the existing methods generally suffer from efficiency issues that obstruct their real-world implementation. In this paper, we introduce duple perturbation robustness, i.e. perturbation on both the feature and factor vectors for low-rank Markov decision processes (MDPs), via a novel characterization of $(xi,eta)$-ambiguity sets. The novel robust MDP formulation is compatible with the function representation view, and therefore, is naturally applicable to practical RL problems with large or even continuous state-action spaces. Meanwhile, it also gives rise to a provably efficient and practical algorithm with theoretical convergence rate guarantee. Examples are designed to justify the new robustness concept, and algorithmic efficiency is supported by both theoretical bounds and numerical simulations. | [
"['Yang Hu' 'Haitong Ma' 'Bo Dai' 'Na Li']"
]
|
null | null | 2404.08091 | null | null | http://arxiv.org/pdf/2404.08091v1 | 2024-04-11T19:13:38Z | 2024-04-11T19:13:38Z | Continual Learning of Range-Dependent Transmission Loss for Underwater
Acoustic using Conditional Convolutional Neural Net | There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce data dimensions effectively. However, current deep-learning models face challenges in predicting wave propagation over long periods and for remote locations, often relying on auto-regressive prediction and lacking far-field bathymetry information. This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. By integrating this architecture into a continual learning framework, we aim to generalize the model for varying bathymetry worldwide. To demonstrate the effectiveness of our approach, we analyze our model on several test cases and a benchmark scenario involving far-field prediction over Dickin's seamount in the Northeast Pacific. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile. This architecture can be integrated into an adaptive management system for underwater radiated noise, providing real-time end-to-end mapping between near-field ship noise sources and received noise at the marine mammal's location. | [
"['Indu Kant Deo' 'Akash Venkateshwaran' 'Rajeev K. Jaiman']"
]
|
null | null | 2404.08093 | null | null | http://arxiv.org/pdf/2404.08093v1 | 2024-04-11T19:15:45Z | 2024-04-11T19:15:45Z | Towards a Robust Soft Baby Robot With Rich Interaction Ability for
Advanced Machine Learning Algorithms | Artificial intelligence has made great strides in many areas lately, yet it has had comparatively little success in general-use robotics. We believe one of the reasons for this is the disconnect between traditional robotic design and the properties needed for open-ended, creativity-based AI systems. To that end, we, taking selective inspiration from nature, build a robust, partially soft robotic limb with a large action space, rich sensory data stream from multiple cameras, and the ability to connect with others to enhance the action space and data stream. As a proof of concept, we train two contemporary machine learning algorithms to perform a simple target-finding task. Altogether, we believe that this design serves as a first step to building a robot tailor-made for achieving artificial general intelligence. | [
"['Mohannad Alhakami' 'Dylan R. Ashley' 'Joel Dunham' 'Francesco Faccio'\n 'Eric Feron' 'Jürgen Schmidhuber']"
]
|
null | null | 2404.08108 | null | null | http://arxiv.org/pdf/2404.08108v2 | 2024-07-11T12:41:51Z | 2024-04-11T20:14:14Z | Protein intrinsic disorder prediction using Attention U-Net and
ProtTrans protein language model | The prediction of intrinsic disorder regions has significant implications for understanding protein function, structure, and dynamics. It can help to discover novel functions or protein-protein interactions essential to designing new drugs, therapies, or enzymes. Recently, a new generation of predictors based on protein language models is emerging. These algorithms reach state-of-the-art accuracy without calculating time-consuming multiple sequence alignments (MSAs). The article pre-sents a new protein intrinsic disorder predictor DisorderUnetLM based on the Attention U-Net convolutional neural network using features from the protein language model ProtTrans. DisorderUnetLM shows top results in the direct comparison with flDPnn and IDP-CRF predictors using MSAs and with the SETH predictor using features from the same ProtTrans model. Moreover, among 41 predictors from the latest Critical Assessment of Protein Intrinsic Disorder Prediction (CAID-2) benchmark, it ranks 9th for the Disorder-PDB subset (with ROC-AUC of 0.924) and 1st for the Disorder-NOX subset (with ROC-AUC of 0.844) which confirms its potential to perform well in the upcoming CAID-3 challenge for which Disor-derUnetLM was submitted. | [
"['Krzysztof Kotowski' 'Irena Roterman' 'Katarzyna Stapor']"
]
|
null | null | 2404.08120 | null | null | http://arxiv.org/pdf/2404.08120v1 | 2024-04-11T20:55:38Z | 2024-04-11T20:55:38Z | A least-square method for non-asymptotic identification in linear
switching control | The focus of this paper is on linear system identification in the setting where it is known that the underlying partially-observed linear dynamical system lies within a finite collection of known candidate models. We first consider the problem of identification from a given trajectory, which in this setting reduces to identifying the index of the true model with high probability. We characterize the finite-time sample complexity of this problem by leveraging recent advances in the non-asymptotic analysis of linear least-square methods in the literature. In comparison to the earlier results that assume no prior knowledge of the system, our approach takes advantage of the smaller hypothesis class and leads to the design of a learner with a dimension-free sample complexity bound. Next, we consider the switching control of linear systems, where there is a candidate controller for each of the candidate models and data is collected through interaction of the system with a collection of potentially destabilizing controllers. We develop a dimension-dependent criterion that can detect those destabilizing controllers in finite time. By leveraging these results, we propose a data-driven switching strategy that identifies the unknown parameters of the underlying system. We then provide a non-asymptotic analysis of its performance and discuss its implications on the classical method of estimator-based supervisory control. | [
"['Haoyuan Sun' 'Ali Jadbabaie']"
]
|
null | null | 2404.08127 | null | null | http://arxiv.org/pdf/2404.08127v1 | 2024-04-11T21:07:38Z | 2024-04-11T21:07:38Z | Self-Supervised Learning of Color Constancy | Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its limitations have been carefully characterized in humans, it is still unclear how the visual system acquires this ability during development. Here, we present a first study showing that CC develops in a neural network trained in a self-supervised manner through an invariance learning objective. During learning, objects are presented under changing illuminations, while the network aims to map subsequent views of the same object onto close-by latent representations. This gives rise to representations that are largely invariant to the illumination conditions, offering a plausible example of how CC could emerge during human cognitive development via a form of self-supervised learning. | [
"['Markus R. Ernst' 'Francisco M. López' 'Arthur Aubret'\n 'Roland W. Fleming' 'Jochen Triesch']"
]
|
null | null | 2404.08131 | null | null | http://arxiv.org/pdf/2404.08131v1 | 2024-04-11T21:24:38Z | 2024-04-11T21:24:38Z | Frame Quantization of Neural Networks | We present a post-training quantization algorithm with error estimates relying on ideas originating from frame theory. Specifically, we use first-order Sigma-Delta ($SigmaDelta$) quantization for finite unit-norm tight frames to quantize weight matrices and biases in a neural network. In our scenario, we derive an error bound between the original neural network and the quantized neural network in terms of step size and the number of frame elements. We also demonstrate how to leverage the redundancy of frames to achieve a quantized neural network with higher accuracy. | [
"['Wojciech Czaja' 'Sanghoon Na']"
]
|
null | null | 2404.08154 | null | null | http://arxiv.org/pdf/2404.08154v1 | 2024-04-11T22:43:44Z | 2024-04-11T22:43:44Z | Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples
Regularization | Single-step adversarial training (SSAT) has demonstrated the potential to achieve both efficiency and robustness. However, SSAT suffers from catastrophic overfitting (CO), a phenomenon that leads to a severely distorted classifier, making it vulnerable to multi-step adversarial attacks. In this work, we observe that some adversarial examples generated on the SSAT-trained network exhibit anomalous behaviour, that is, although these training samples are generated by the inner maximization process, their associated loss decreases instead, which we named abnormal adversarial examples (AAEs). Upon further analysis, we discover a close relationship between AAEs and classifier distortion, as both the number and outputs of AAEs undergo a significant variation with the onset of CO. Given this observation, we re-examine the SSAT process and uncover that before the occurrence of CO, the classifier already displayed a slight distortion, indicated by the presence of few AAEs. Furthermore, the classifier directly optimizing these AAEs will accelerate its distortion, and correspondingly, the variation of AAEs will sharply increase as a result. In such a vicious circle, the classifier rapidly becomes highly distorted and manifests as CO within a few iterations. These observations motivate us to eliminate CO by hindering the generation of AAEs. Specifically, we design a novel method, termed Abnormal Adversarial Examples Regularization (AAER), which explicitly regularizes the variation of AAEs to hinder the classifier from becoming distorted. Extensive experiments demonstrate that our method can effectively eliminate CO and further boost adversarial robustness with negligible additional computational overhead. | [
"['Runqi Lin' 'Chaojian Yu' 'Tongliang Liu']"
]
|
null | null | 2404.08158 | null | null | http://arxiv.org/abs/2404.08158v1 | 2024-04-11T23:16:21Z | 2024-04-11T23:16:21Z | On the Power of Interactive Proofs for Learning | We continue the study of doubly-efficient proof systems for verifying agnostic PAC learning, for which we obtain the following results. - We construct an interactive protocol for learning the $t$ largest Fourier characters of a given function $f colon {0,1}^n to {0,1}$ up to an arbitrarily small error, wherein the verifier uses $mathsf{poly}(t)$ random examples. This improves upon the Interactive Goldreich-Levin protocol of Goldwasser, Rothblum, Shafer, and Yehudayoff (ITCS 2021) whose sample complexity is $mathsf{poly}(t,n)$. - For agnostically learning the class $mathsf{AC}^0[2]$ under the uniform distribution, we build on the work of Carmosino, Impagliazzo, Kabanets, and Kolokolova (APPROX/RANDOM 2017) and design an interactive protocol, where given a function $f colon {0,1}^n to {0,1}$, the verifier learns the closest hypothesis up to $mathsf{polylog}(n)$ multiplicative factor, using quasi-polynomially many random examples. In contrast, this class has been notoriously resistant even for constructing realisable learners (without a prover) using random examples. - For agnostically learning $k$-juntas under the uniform distribution, we obtain an interactive protocol, where the verifier uses $O(2^k)$ random examples to a given function $f colon {0,1}^n to {0,1}$. Crucially, the sample complexity of the verifier is independent of $n$. We also show that if we do not insist on doubly-efficient proof systems, then the model becomes trivial. Specifically, we show a protocol for an arbitrary class $mathcal{C}$ of Boolean functions in the distribution-free setting, where the verifier uses $O(1)$ labeled examples to learn $f$. | [
"['Tom Gur' 'Mohammad Mahdi Jahanara' 'Mohammad Mahdi Khodabandeh'\n 'Ninad Rajgopal' 'Bahar Salamatian' 'Igor Shinkar']"
]
|
null | null | 2404.08164 | null | null | http://arxiv.org/pdf/2404.08164v2 | 2024-05-20T02:34:42Z | 2024-04-12T00:03:56Z | Language Model Prompt Selection via Simulation Optimization | With the advancement in generative language models, the selection of prompts has gained significant attention in recent years. A prompt is an instruction or description provided by the user, serving as a guide for the generative language model in content generation. Despite existing methods for prompt selection that are based on human labor, we consider facilitating this selection through simulation optimization, aiming to maximize a pre-defined score for the selected prompt. Specifically, we propose a two-stage framework. In the first stage, we determine a feasible set of prompts in sufficient numbers, where each prompt is represented by a moderate-dimensional vector. In the subsequent stage for evaluation and selection, we construct a surrogate model of the score regarding the moderate-dimensional vectors that represent the prompts. We propose sequentially selecting the prompt for evaluation based on this constructed surrogate model. We prove the consistency of the sequential evaluation procedure in our framework. We also conduct numerical experiments to demonstrate the efficacy of our proposed framework, providing practical instructions for implementation. | [
"['Haoting Zhang' 'Jinghai He' 'Rhonda Righter' 'Zeyu Zheng']"
]
|
null | null | 2404.08168 | null | null | http://arxiv.org/pdf/2404.08168v1 | 2024-04-12T00:21:30Z | 2024-04-12T00:21:30Z | Conformal Prediction via Regression-as-Classification | Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals.~Here, we circumvent the challenges by converting regression to a classification problem and then use CP for classification to obtain CP sets for regression.~To preserve the ordering of the continuous-output space, we design a new loss function and make necessary modifications to the CP classification techniques.~Empirical results on many benchmarks shows that this simple approach gives surprisingly good results on many practical problems. | [
"['Etash Guha' 'Shlok Natarajan' 'Thomas Möllenhoff'\n 'Mohammad Emtiyaz Khan' 'Eugene Ndiaye']"
]
|
null | null | 2404.08172 | null | null | http://arxiv.org/pdf/2404.08172v1 | 2024-04-12T00:39:53Z | 2024-04-12T00:39:53Z | Optimal Universal Quantum Encoding for Statistical Inference | Optimal encoding of classical data for statistical inference using quantum computing is investigated. A universal encoder is sought that is optimal for a wide array of statistical inference tasks. Accuracy of any statistical inference is shown to be upper bounded by a term that is proportional to maximal quantum leakage from the classical data, i.e., the input to the inference model, through its quantum encoding. This demonstrates that the maximal quantum leakage is a universal measure of the quality of the encoding strategy for statistical inference as it only depends on the quantum encoding of the data and not the inference task itself. The optimal universal encoding strategy, i.e., the encoding strategy that maximizes the maximal quantum leakage, is proved to be attained by pure states. When there are enough qubits, basis encoding is proved to be universally optimal. An iterative method for numerically computing the optimal universal encoding strategy is presented. | [
"['Farhad Farokhi']"
]
|
null | null | 2404.08176 | null | null | http://arxiv.org/pdf/2404.08176v1 | 2024-04-12T00:55:07Z | 2024-04-12T00:55:07Z | Introducing Graph Learning over Polytopic Uncertain Graph | This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating this assumption that the graph lies in a polytopic set into two established graph learning frameworks, we find that our approach yields better results with less computation. | [
"['Masako Kishida' 'Shunsuke Ono']"
]
|
null | null | 2404.08189 | null | null | http://arxiv.org/pdf/2404.08189v1 | 2024-04-12T01:42:09Z | 2024-04-12T01:42:09Z | Reducing hallucination in structured outputs via Retrieval-Augmented
Generation | A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While large language models (LLM) have taken the world by storm, without eliminating or at least reducing hallucinations, real-world GenAI systems may face challenges in user adoption. In the process of deploying an enterprise application that produces workflows based on natural language requirements, we devised a system leveraging Retrieval Augmented Generation (RAG) to greatly improve the quality of the structured output that represents such workflows. Thanks to our implementation of RAG, our proposed system significantly reduces hallucinations in the output and improves the generalization of our LLM in out-of-domain settings. In addition, we show that using a small, well-trained retriever encoder can reduce the size of the accompanying LLM, thereby making deployments of LLM-based systems less resource-intensive. | [
"['Patrice Béchard' 'Orlando Marquez Ayala']"
]
|
null | null | 2404.08221 | null | null | http://arxiv.org/pdf/2404.08221v1 | 2024-03-31T22:10:01Z | 2024-03-31T22:10:01Z | Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues
in Generative AI | In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers. With this in mind, this survey provides an extensive examination of copyright infringement as it pertains to generative AI, aiming to stay abreast of the latest developments and open problems. Specifically, it will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models. Furthermore, this survey will discuss resources and tools for users to evaluate copyright violations. Finally, insights into ongoing regulations and proposals for AI will be explored and compared. Through combining these disciplines, the implications of AI-driven content and copyright are thoroughly illustrated and brought into question. | [
"['Jocelyn Dzuong' 'Zichong Wang' 'Wenbin Zhang']"
]
|
null | null | 2404.08224 | null | null | http://arxiv.org/pdf/2404.08224v2 | 2024-04-18T04:15:35Z | 2024-04-12T03:39:33Z | HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate
Detection of Industrial Multivariate Time Series Anomalies | Multivariate Time Series (MTS) anomaly detection focuses on pinpointing samples that diverge from standard operational patterns, which is crucial for ensuring the safety and security of industrial applications. The primary challenge in this domain is to develop representations capable of discerning anomalies effectively. The prevalent methods for anomaly detection in the literature are predominantly reconstruction-based and predictive in nature. However, they typically concentrate on a single-dimensional instance level, thereby not fully harnessing the complex associations inherent in industrial MTS. To address this issue, we propose a novel self-supervised hierarchical contrastive consistency learning method for detecting anomalies in MTS, named HCL-MTSAD. It innovatively leverages data consistency at multiple levels inherent in industrial MTS, systematically capturing consistent associations across four latent levels-measurement, sample, channel, and process. By developing a multi-layer contrastive loss, HCL-MTSAD can extensively mine data consistency and spatio-temporal association, resulting in more informative representations. Subsequently, an anomaly discrimination module, grounded in self-supervised hierarchical contrastive learning, is designed to detect timestamp-level anomalies by calculating multi-scale data consistency. Extensive experiments conducted on six diverse MTS datasets retrieved from real cyber-physical systems and server machines, in comparison with 20 baselines, indicate that HCL-MTSAD's anomaly detection capability outperforms the state-of-the-art benchmark models by an average of 1.8% in terms of F1 score. | [
"['Haili Sun' 'Yan Huang' 'Lansheng Han' 'Cai Fu' 'Chunjie Zhou']"
]
|
null | null | 2404.08230 | null | null | http://arxiv.org/pdf/2404.08230v1 | 2024-04-12T04:17:50Z | 2024-04-12T04:17:50Z | Enhancing Fairness and Performance in Machine Learning Models: A
Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality | This paper considers the need for generalizable bias mitigation techniques in machine learning due to the growing concerns of fairness and discrimination in data-driven decision-making procedures across a range of industries. While many existing methods for mitigating bias in machine learning have succeeded in specific cases, they often lack generalizability and cannot be easily applied to different data types or models. Additionally, the trade-off between accuracy and fairness remains a fundamental tension in the field. To address these issues, we propose a bias mitigation method based on multi-task learning, utilizing the concept of Monte-Carlo dropout and Pareto optimality from multi-objective optimization. This method optimizes accuracy and fairness while improving the model's explainability without using sensitive information. We test this method on three datasets from different domains and show how it can deliver the most desired trade-off between model fairness and performance. This allows for tuning in specific domains where one metric may be more important than another. With the framework we introduce in this paper, we aim to enhance the fairness-performance trade-off and offer a solution to bias mitigation methods' generalizability issues in machine learning. | [
"['Khadija Zanna' 'Akane Sano']"
]
|
null | null | 2404.08233 | null | null | http://arxiv.org/pdf/2404.08233v2 | 2024-04-23T03:28:31Z | 2024-04-12T04:23:20Z | Generalized Population-Based Training for Hyperparameter Optimization in
Reinforcement Learning | Hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environments, requiring dynamic adjustments in their learning trajectories. To cater to this dynamicity, the Population-Based Training (PBT) was introduced, leveraging the collective intelligence of a population of agents learning simultaneously. However, PBT tends to favor high-performing agents, potentially neglecting the explorative potential of agents on the brink of significant advancements. To mitigate the limitations of PBT, we present the Generalized Population-Based Training (GPBT), a refined framework designed for enhanced granularity and flexibility in hyperparameter adaptation. Complementing GPBT, we further introduce Pairwise Learning (PL). Instead of merely focusing on elite agents, PL employs a comprehensive pairwise strategy to identify performance differentials and provide holistic guidance to underperforming agents. By integrating the capabilities of GPBT and PL, our approach significantly improves upon traditional PBT in terms of adaptability and computational efficiency. Rigorous empirical evaluations across a range of RL benchmarks confirm that our approach consistently outperforms not only the conventional PBT but also its Bayesian-optimized variant. | [
"['Hui Bai' 'Ran Cheng']"
]
|
null | null | 2404.08246 | null | null | http://arxiv.org/pdf/2404.08246v1 | 2024-04-12T05:25:03Z | 2024-04-12T05:25:03Z | Agile and versatile bipedal robot tracking control through reinforcement
learning | The remarkable athletic intelligence displayed by humans in complex dynamic movements such as dancing and gymnastics suggests that the balance mechanism in biological beings is decoupled from specific movement patterns. This decoupling allows for the execution of both learned and unlearned movements under certain constraints while maintaining balance through minor whole-body coordination. To replicate this balance ability and body agility, this paper proposes a versatile controller for bipedal robots. This controller achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network, which is based on a model-based IK solver and reinforcement learning. We consider a single step as the smallest control unit and design a universally applicable control input form suitable for any single-step variation. Highly flexible gait control can be achieved by combining these minimal control units with high-level policy through our extensible control interface. To enhance the trajectory-tracking capability of our controller, we utilize a three-stage training curriculum. After training, the robot can move freely between target footholds at varying distances and heights. The robot can also maintain static balance without repeated stepping to adjust posture. Finally, we evaluate the tracking accuracy of our controller on various bipedal tasks, and the effectiveness of our control framework is verified in the simulation environment. | [
"['Jiayi Li' 'Linqi Ye' 'Yi Cheng' 'Houde Liu' 'Bin Liang']"
]
|
null | null | 2404.08254 | null | null | http://arxiv.org/pdf/2404.08254v1 | 2024-04-12T06:08:43Z | 2024-04-12T06:08:43Z | Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models | Diffusion models have emerged as a robust framework for various generative tasks, such as image and audio synthesis, and have also demonstrated a remarkable ability to generate mixed-type tabular data comprising both continuous and discrete variables. However, current approaches to training diffusion models on mixed-type tabular data tend to inherit the imbalanced distributions of features present in the training dataset, which can result in biased sampling. In this research, we introduce a fair diffusion model designed to generate balanced data on sensitive attributes. We present empirical evidence demonstrating that our method effectively mitigates the class imbalance in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data in terms of performance and fairness. | [
"['Zeyu Yang' 'Peikun Guo' 'Khadija Zanna' 'Akane Sano']"
]
|
null | null | 2404.08263 | null | null | http://arxiv.org/pdf/2404.08263v1 | 2024-04-12T06:23:07Z | 2024-04-12T06:23:07Z | Relational Prompt-based Pre-trained Language Models for Social Event
Detection | Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with noisy and missing edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM_SED on three real-world datasets, demonstrating that the RPLM_SED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks. | [
"['Pu Li' 'Xiaoyan Yu' 'Hao Peng' 'Yantuan Xian' 'Linqin Wang' 'Li Sun'\n 'Jingyun Zhang' 'Philip S. Yu']"
]
|
null | null | 2404.08271 | null | null | http://arxiv.org/pdf/2404.08271v2 | 2024-07-09T09:46:06Z | 2024-04-12T06:50:32Z | Transfer Learning Study of Motion Transformer-based Trajectory
Predictions | Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition to the shifts from simulation to the real world, many vehicle- and country-specific shifts, i.e. differences in sensor systems, fusion and perception algorithms as well as traffic rules and laws, are on the agenda. Since models that can cover all system setups and design domains at once are not yet foreseeable, model adaptation plays a central role. Therefore, a simulation-based study on transfer learning techniques is conducted on basis of a transformer-based model. Furthermore, the study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world. | [
"['Lars Ullrich' 'Alex McMaster' 'Knut Graichen']"
]
|
null | null | 2404.08279 | null | null | http://arxiv.org/pdf/2404.08279v1 | 2024-04-12T07:08:05Z | 2024-04-12T07:08:05Z | Convolutional neural network classification of cancer cytopathology
images: taking breast cancer as an example | Breast cancer is a relatively common cancer among gynecological cancers. Its diagnosis often relies on the pathology of cells in the lesion. The pathological diagnosis of breast cancer not only requires professionals and time, but also sometimes involves subjective judgment. To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection. And the approach enables the rapid and automatic classification of pathological images into benign and malignant groups. The methodology involves utilizing a convolutional neural network (CNN) model leveraging the Inceptionv3 architecture and transfer learning algorithm for extracting features from pathological images. Utilizing a neural network with fully connected layers and employing the SoftMax function for image classification. Additionally, the concept of image partitioning is introduced to handle high-resolution images. To achieve the ultimate classification outcome, the classification probabilities of each image block are aggregated using three algorithms: summation, product, and maximum. Experimental validation was conducted on the BreaKHis public dataset, resulting in accuracy rates surpassing 0.92 across all four magnification coefficients (40X, 100X, 200X, and 400X). It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer. | [
"['MingXuan Xiao' 'Yufeng Li' 'Xu Yan' 'Min Gao' 'Weimin Wang']"
]
|
null | null | 2404.08295 | null | null | http://arxiv.org/pdf/2404.08295v1 | 2024-04-12T07:34:46Z | 2024-04-12T07:34:46Z | Study of Emotion Concept Formation by Integrating Vision, Physiology,
and Word Information using Multilayered Multimodal Latent Dirichlet
Allocation | How are emotions formed? Through extensive debate and the promulgation of diverse theories , the theory of constructed emotion has become prevalent in recent research on emotions. According to this theory, an emotion concept refers to a category formed by interoceptive and exteroceptive information associated with a specific emotion. An emotion concept stores past experiences as knowledge and can predict unobserved information from acquired information. Therefore, in this study, we attempted to model the formation of emotion concepts using a constructionist approach from the perspective of the constructed emotion theory. Particularly, we constructed a model using multilayered multimodal latent Dirichlet allocation , which is a probabilistic generative model. We then trained the model for each subject using vision, physiology, and word information obtained from multiple people who experienced different visual emotion-evoking stimuli. To evaluate the model, we verified whether the formed categories matched human subjectivity and determined whether unobserved information could be predicted via categories. The verification results exceeded chance level, suggesting that emotion concept formation can be explained by the proposed model. | [
"['Kazuki Tsurumaki' 'Chie Hieida' 'Kazuki Miyazawa']"
]
|
null | null | 2404.08301 | null | null | http://arxiv.org/abs/2404.08301v1 | 2024-04-12T07:47:02Z | 2024-04-12T07:47:02Z | Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile
Games under Consumption Uncertainty | With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending data to mitigate label variance and extremes, ensuring stability in the modeling process. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs, thus ensuring user privacy and enabling seamless online training. Our model adopts a unique approach by separately representing user preferences and game features before merging them as input to the spending prediction module. Through rigorous experimentation, our approach demonstrates notable improvements over production models, achieving a remarkable textbf{17.11}% enhancement on offline data and an impressive textbf{50.65}% boost in an online A/B test. In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming. | [
"['Peijie Sun' 'Yifan Wang' 'Min Zhang' 'Chuhan Wu' 'Yan Fang' 'Hong Zhu'\n 'Yuan Fang' 'Meng Wang']"
]
|
null | null | 2404.08303 | null | null | http://arxiv.org/pdf/2404.08303v1 | 2024-04-12T07:51:21Z | 2024-04-12T07:51:21Z | A Large Scale Survey of Motivation in Software Development and Analysis
of its Validity | Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since motivation is an internal subjective feeling, we also analyze the validity of the answers. Method: We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11 point scale. We evaluated the validity of the answers validity by comparing related questions, comparing to actual behavior on GitHub, and comparison with the same developer in a follow up survey. Results: Validity problems include moderate correlations between answers to related questions, as well as self promotion and mistakes in the answers. Despite these problems, predictive analysis, investigating how diverse motivators influence the probability of high motivation, provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation. | [
"['Idan Amit' 'Dror G. Feitelson']"
]
|
null | null | 2404.08314 | null | null | http://arxiv.org/pdf/2404.08314v1 | 2024-04-12T08:20:01Z | 2024-04-12T08:20:01Z | Multi-Step Traffic Prediction for Multi-Period Planning in Optical
Networks | A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while minimizing undesired service disruptions (caused due to lightpath re-allocations), with these heuristics outperforming a single-step ahead prediction approach. | [
"['Hafsa Maryam' 'Tania Panayiotou' 'Georgios Ellinas']"
]
|
null | null | 2404.08325 | null | null | http://arxiv.org/pdf/2404.08325v1 | 2024-04-12T08:35:38Z | 2024-04-12T08:35:38Z | Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite
Data | Deep neural networks (DNNs) have been successfully applied to earth observation (EO) data and opened new research avenues. Despite the theoretical and practical advances of these techniques, DNNs are still considered black box tools and by default are designed to give point predictions. However, the majority of EO applications demand reliable uncertainty estimates that can support practitioners in critical decision making tasks. This work provides a theoretical and quantitative comparison of existing uncertainty quantification methods for DNNs applied to the task of wind speed estimation in satellite imagery of tropical cyclones. We provide a detailed evaluation of predictive uncertainty estimates from state-of-the-art uncertainty quantification (UQ) methods for DNNs. We find that predictive uncertainties can be utilized to further improve accuracy and analyze the predictive uncertainties of different methods across storm categories. | [
"['Nils Lehmann' 'Nina Maria Gottschling' 'Stefan Depeweg' 'Eric Nalisnick']"
]
|
null | null | 2404.08335 | null | null | http://arxiv.org/pdf/2404.08335v1 | 2024-04-12T09:01:14Z | 2024-04-12T09:01:14Z | Toward a Theory of Tokenization in LLMs | While there has been a large body of research attempting to circumvent tokenization for language modeling (Clark et al., 2022; Xue et al., 2022), the current consensus is that it is a necessary initial step for designing state-of-the-art performant language models. In this paper, we investigate tokenization from a theoretical point of view by studying the behavior of transformers on simple data generating processes. When trained on data drawn from certain simple $k^{text{th}}$-order Markov processes for $k > 1$, transformers exhibit a surprising phenomenon - in the absence of tokenization, they empirically fail to learn the right distribution and predict characters according to a unigram model (Makkuva et al., 2024). With the addition of tokenization, however, we empirically observe that transformers break through this barrier and are able to model the probabilities of sequences drawn from the source near-optimally, achieving small cross-entropy loss. With this observation as starting point, we study the end-to-end cross-entropy loss achieved by transformers with and without tokenization. With the appropriate tokenization, we show that even the simplest unigram models (over tokens) learnt by transformers are able to model the probability of sequences drawn from $k^{text{th}}$-order Markov sources near optimally. Our analysis provides a justification for the use of tokenization in practice through studying the behavior of transformers on Markovian data. | [
"['Nived Rajaraman' 'Jiantao Jiao' 'Kannan Ramchandran']"
]
|
null | null | 2404.08347 | null | null | http://arxiv.org/pdf/2404.08347v1 | 2024-04-12T09:22:24Z | 2024-04-12T09:22:24Z | Learning to Rebalance Multi-Modal Optimization by Adaptively Masking
Subnetworks | Multi-modal learning aims to enhance performance by unifying models from various modalities but often faces the "modality imbalance" problem in real data, leading to a bias towards dominant modalities and neglecting others, thereby limiting its overall effectiveness. To address this challenge, the core idea is to balance the optimization of each modality to achieve a joint optimum. Existing approaches often employ a modal-level control mechanism for adjusting the update of each modal parameter. However, such a global-wise updating mechanism ignores the different importance of each parameter. Inspired by subnetwork optimization, we explore a uniform sampling-based optimization strategy and find it more effective than global-wise updating. According to the findings, we further propose a novel importance sampling-based, element-wise joint optimization method, called Adaptively Mask Subnetworks Considering Modal Significance(AMSS). Specifically, we incorporate mutual information rates to determine the modal significance and employ non-uniform adaptive sampling to select foreground subnetworks from each modality for parameter updates, thereby rebalancing multi-modal learning. Additionally, we demonstrate the reliability of the AMSS strategy through convergence analysis. Building upon theoretical insights, we further enhance the multi-modal mask subnetwork strategy using unbiased estimation, referred to as AMSS+. Extensive experiments reveal the superiority of our approach over comparison methods. | [
"['Yang Yang' 'Hongpeng Pan' 'Qing-Yuan Jiang' 'Yi Xu' 'Jinghui Tang']"
]
|
null | null | 2404.08350 | null | null | http://arxiv.org/pdf/2404.08350v1 | 2024-04-12T09:31:11Z | 2024-04-12T09:31:11Z | Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI
Using Neural Implicit k-Space Representation | Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we incorporate as novel self-supervised k-space regularization enforcing a consistent neighborhood relationship. At no additional data cost, the proposed regularization significantly improves neural implicit k-space reconstructions on simulated data. Abdominal in-vivo reconstructions using PISCO result in enhanced spatio-temporal image quality compared to state-of-the-art methods. Code is available at https://github.com/vjspi/PISCO-NIK. | [
"['Veronika Spieker' 'Hannah Eichhorn' 'Jonathan K. Stelter' 'Wenqi Huang'\n 'Rickmer F. Braren' 'Daniel Rückert' 'Francisco Sahli Costabal'\n 'Kerstin Hammernik' 'Claudia Prieto' 'Dimitrios C. Karampinos'\n 'Julia A. Schnabel']"
]
|
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