categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2407.02489 | null | null | http://arxiv.org/pdf/2407.02489v1 | 2024-07-02T17:59:50Z | 2024-07-02T17:59:50Z | Magic Insert: Style-Aware Drag-and-Drop | We present Magic Insert, a method for dragging-and-dropping subjects from a user-provided image into a target image of a different style in a physically plausible manner while matching the style of the target image. This work formalizes the problem of style-aware drag-and-drop and presents a method for tackling it by addressing two sub-problems: style-aware personalization and realistic object insertion in stylized images. For style-aware personalization, our method first fine-tunes a pretrained text-to-image diffusion model using LoRA and learned text tokens on the subject image, and then infuses it with a CLIP representation of the target style. For object insertion, we use Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object insertion model to the domain of diverse artistic styles. Overall, the method significantly outperforms traditional approaches such as inpainting. Finally, we present a dataset, SubjectPlop, to facilitate evaluation and future progress in this area. Project page: https://magicinsert.github.io/ | [
"['Nataniel Ruiz' 'Yuanzhen Li' 'Neal Wadhwa' 'Yael Pritch'\n 'Michael Rubinstein' 'David E. Jacobs' 'Shlomi Fruchter']"
] |
null | null | 2407.02490 | null | null | http://arxiv.org/pdf/2407.02490v1 | 2024-07-02T17:59:56Z | 2024-07-02T17:59:56Z | MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via
Dynamic Sparse Attention | The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparsethat can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference. | [
"['Huiqiang Jiang' 'Yucheng Li' 'Chengruidong Zhang' 'Qianhui Wu'\n 'Xufang Luo' 'Surin Ahn' 'Zhenhua Han' 'Amir H. Abdi' 'Dongsheng Li'\n 'Chin-Yew Lin' 'Yuqing Yang' 'Lili Qiu']"
] |
null | null | 2407.02501 | null | null | http://arxiv.org/pdf/2407.02501v1 | 2024-06-10T22:22:41Z | 2024-06-10T22:22:41Z | Data-driven Power Flow Linearization: Theory | This two-part tutorial dives into the field of data-driven power flow linearization (DPFL), a domain gaining increased attention. DPFL stands out for its higher approximation accuracy, wide adaptability, and better ability to implicitly incorporate the latest system attributes. This renders DPFL a potentially superior option for managing the significant fluctuations from renewable energy sources, a step towards realizing a more sustainable energy future, by translating the higher model accuracy into increased economic efficiency and less energy losses. To conduct a deep and rigorous reexamination, this tutorial first classifies existing DPFL methods into DPFL training algorithms and supportive techniques. Their mathematical models, analytical solutions, capabilities, limitations, and generalizability are systematically examined, discussed, and summarized. In addition, this tutorial reviews existing DPFL experiments, examining the settings of test systems, the fidelity of datasets, and the comparison made among a limited number of DPFL methods. Further, this tutorial implements extensive numerical comparisons of all existing DPFL methods (40 methods in total) and four classic physics-driven approaches, focusing on their generalizability, applicability, accuracy, and computational efficiency. Through these simulationmethodss, this tutorial aims to reveal the actual performance of all the methods (including the performances exposed to data noise or outliers), guiding the selection of appropriate linearization methods. Furthermore, this tutorial discusses future directions based on the theoretical and numerical insights gained. As the first part, this paper reexamines DPFL theories, covering all the training algorithms and supportive techniques. Capabilities, limitations, and aspects of generalizability, which were previously unmentioned in the literature, have been identified. | [
"['Mengshuo Jia' 'Gabriela Hug' 'Ning Zhang' 'Zhaojian Wang' 'Yi Wang'\n 'Chongqing Kang']"
] |
null | null | 2407.02505 | null | null | http://arxiv.org/pdf/2407.02505v1 | 2024-06-16T11:27:43Z | 2024-06-16T11:27:43Z | A MgNO Method for Multiphase Flow in Porous Media | This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. | [
"['Xinliang Liu' 'Xia Yang' 'Chen-Song Zhang' 'Lian Zhang' 'Li Zhao']"
] |
null | null | 2407.02508 | null | null | http://arxiv.org/pdf/2407.02508v1 | 2024-06-18T14:27:14Z | 2024-06-18T14:27:14Z | Sample-efficient Imitative Multi-token Decision Transformer for
Generalizable Real World Driving | Reinforcement learning via sequence modeling has shown remarkable promise in autonomous systems, harnessing the power of offline datasets to make informed decisions in simulated environments. However, the full potential of such methods in complex dynamic environments remain to be discovered. In autonomous driving domain, learning-based agents face significant challenges when transferring knowledge from simulated to real-world settings and the performance is also significantly impacted by data distribution shift. To address these issue, we propose Sample-efficient Imitative Multi-token Decision Transformer (SimDT). SimDT introduces multi-token prediction, imitative online learning and prioritized experience replay to Decision Transformer. The performance is evaluated through empirical experiments and results exceed popular imitation and reinforcement learning algorithms on Waymax benchmark. | [
"['Hang Zhou' 'Dan Xu' 'Yiding Ji']"
] |
null | null | 2407.02509 | null | null | http://arxiv.org/abs/2407.02509v1 | 2024-06-18T16:02:29Z | 2024-06-18T16:02:29Z | Variables are a Curse in Software Vulnerability Prediction | Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and thus could learn a representation that is only specific to the code text, rather than the representation that depicts the 'intrinsic' functionality of a program hidden in the text representation. One curse that causes this problem is an infinite number of possibilities to name a variable. In order to lift the curse, in this work we introduce a new type of edge called name dependence, a type of abstract syntax graph based on the name dependence, and an efficient node representation method named 3-property encoding scheme. These techniques will allow us to remove the concrete variable names from code, and facilitate deep learning models to learn the functionality of software hidden in diverse code expressions. The experimental results show that the deep learning models built on these techniques outperform the ones based on existing approaches not only in the prediction of vulnerabilities but also in the memory need. The factor of memory usage reductions of our techniques can be up to the order of 30,000 in comparison to existing approaches. | [
"['Jinghua Groppe' 'Sven Groppe' 'Ralf Möller']"
] |
null | null | 2407.02510 | null | null | http://arxiv.org/pdf/2407.02510v1 | 2024-06-19T15:00:02Z | 2024-06-19T15:00:02Z | Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional
Coverage Closure | Novel test selectors have demonstrated their effectiveness in accelerating the closure of functional coverage for various industrial digital designs in simulation-based verification. The primary advantages of these test selectors include performance that is not impacted by coverage holes, straightforward implementation, and relatively low computational expense. However, the detection of stimuli with novel temporal patterns remains largely unexplored. This paper introduces two novel test selectors designed to identify such stimuli. The experiments reveal that both test selectors can accelerate the functional coverage for a commercial bus bridge, compared to random test selection. Specifically, one selector achieves a 26.9% reduction in the number of simulated tests required to reach 98.5% coverage, outperforming the savings achieved by two previously published test selectors by factors of 13 and 2.68, respectively. | [
"['Xuan Zheng' 'Tim Blackmore' 'James Buckingham' 'Kerstin Eder']"
] |
null | null | 2407.02519 | null | null | http://arxiv.org/pdf/2407.02519v1 | 2024-06-24T15:29:22Z | 2024-06-24T15:29:22Z | Anvil: An integration of artificial intelligence, sampling techniques,
and a combined CAD-CFD tool | In this work, we introduce an open-source integrated CAD-CFD tool, Anvil, which combines FreeCAD for CAD modeling and OpenFOAM for CFD analysis, along with an AI-based optimization method (Bayesian optimization) and other sampling algorithms. Anvil serves as a scientific machine learning tool for shape optimization in three modes: data generation, CFD evaluation, and shape optimization. In data generation mode, it automatically runs CFD evaluations and generates data for training a surrogate model. In optimization mode, it searches for the optimal design under given requirements and optimization metrics. In CFD mode, a single CAD file can be evaluated with a single OpenFOAM run. To use Anvil, experimenters provide a JSON configuration file and a parametric CAD seed design. Anvil can be used to study solid-fluid dynamics for any subsonic flow conditions and has been demonstrated in various simulation and optimization use cases. The open-source code for the tool, installation process, artifacts (such as CAD seed designs and example STL models), experimentation results, and detailed documentation can be found at url{https://github.com/symbench/Anvil}. | [
"['Harsh Vardhan' 'Umesh Timalsina' 'Michael Sandborn' 'David Hyde'\n 'Peter Volgyesi' 'Janos Sztipanovits']"
] |
null | null | 2407.02520 | null | null | http://arxiv.org/pdf/2407.02520v1 | 2024-06-24T17:43:24Z | 2024-06-24T17:43:24Z | RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite
Imitation Learning | In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning approach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the significant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments. | [
"['Harsh Bansal' 'Vyom Goyal' 'Bhaskar Joshi' 'Akhil Gupta'\n 'Harikumar Kandath']"
] |
null | null | 2407.02521 | null | null | http://arxiv.org/pdf/2407.02521v1 | 2024-06-25T07:49:25Z | 2024-06-25T07:49:25Z | Performance Comparison of Deep RL Algorithms for Mixed Traffic
Cooperative Lane-Changing | Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforcement learning (DRL) approaches, leveraging their data-driven and model-free nature. Our previous work proposed a cooperative lane-changing in mixed traffic (CLCMT) mechanism based on TD3 to facilitate an optimal lane-changing strategy. This study enhances the current CLCMT mechanism by considering both the uncertainty of the human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs. The state-of-the-art (SOTA) DRL algorithms including DDPG, TD3, SAC, and PPO are utilized to deal with the formulated MDP with continuous actions. Performance comparison among the four DRL algorithms demonstrates that DDPG, TD3, and PPO algorithms can deal with uncertainty in traffic environments and learn well-performed LC strategies in terms of safety, efficiency, comfort, and ecology. The PPO algorithm outperforms the other three algorithms, regarding a higher reward, fewer exploration mistakes and crashes, and a more comfortable and ecology LC strategy. The improvements promise CLCMT mechanism greater advantages in the LC motion planning of CAVs. | [
"['Xue Yao' 'Shengren Hou' 'Serge P. Hoogendoorn' 'Simeon C. Calvert']"
] |
null | null | 2407.02536 | null | null | http://arxiv.org/abs/2407.02536v1 | 2024-07-01T21:03:04Z | 2024-07-01T21:03:04Z | Reducing False Discoveries in Statistically-Significant
Regional-Colocation Mining: A Summary of Results | Given a set emph{S} of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs $<$a region ($r_{g}$), a subset emph{C} of emph{S}$>$ such that emph{C} is a statistically significant regional-colocation pattern in $r_{g}$. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner cite{10.1145/3557989.3566158} that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost. | [
"['Subhankar Ghosh' 'Jayant Gupta' 'Arun Sharma' 'Shuai An'\n 'Shashi Shekhar']"
] |
null | null | 2407.02538 | null | null | http://arxiv.org/pdf/2407.02538v1 | 2024-07-01T23:24:05Z | 2024-07-01T23:24:05Z | CGRclust: Chaos Game Representation for Twin Contrastive Clustering of
Unlabelled DNA Sequences | This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility. | [
"['Fatemeh Alipour' 'Kathleen A. Hill' 'Lila Kari']"
] |
null | null | 2407.02539 | null | null | http://arxiv.org/pdf/2407.02539v2 | 2024-07-08T16:50:48Z | 2024-07-02T00:44:06Z | Research on Autonomous Robots Navigation based on Reinforcement Learning | Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become one of the key methods to achieve autonomous navigation of robots. In this work, an autonomous robot navigation method based on reinforcement learning is introduced. We use the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback. By combining the Q-value function with the deep neural network, deep Q network can handle high-dimensional state space, so as to realize path planning in complex environments. Proximal policy optimization is a strategy gradient-based method, which enables robots to explore and utilize environmental information more efficiently by optimizing policy functions. These methods not only improve the robot's navigation ability in the unknown environment, but also enhance its adaptive and self-learning capabilities. Through multiple training and simulation experiments, we have verified the effectiveness and robustness of these models in various complex scenarios. | [
"['Zixiang Wang' 'Hao Yan' 'Yining Wang' 'Zhengjia Xu' 'Zhuoyue Wang'\n 'Zhizhong Wu']"
] |
null | null | 2407.02540 | null | null | http://arxiv.org/pdf/2407.02540v1 | 2024-07-02T01:59:34Z | 2024-07-02T01:59:34Z | Analytical Solution of a Three-layer Network with a Matrix Exponential
Activation Function | In practice, deeper networks tend to be more powerful than shallow ones, but this has not been understood theoretically. In this paper, we find the analytical solution of a three-layer network with a matrix exponential activation function, i.e., $$ f(X)=W_3exp(W_2exp(W_1X)), Xin mathbb{C}^{dtimes d} $$ have analytical solutions for the equations $$ Y_1=f(X_1),Y_2=f(X_2) $$ for $X_1,X_2,Y_1,Y_2$ with only invertible assumptions. Our proof shows the power of depth and the use of a non-linear activation function, since one layer network can only solve one equation,i.e.,$Y=WX$. | [
"['Kuo Gai' 'Shihua Zhang']"
] |
null | null | 2407.02542 | null | null | http://arxiv.org/pdf/2407.02542v1 | 2024-07-02T07:02:39Z | 2024-07-02T07:02:39Z | ECAT: A Entire space Continual and Adaptive Transfer Learning Framework
for Cross-Domain Recommendation | In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of the entire space, making it challenging to train an efficient model. In recent years, there have been many excellent studies related to cross-domain recommendation aimed at mitigating the problem of data sparsity. However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. Specifically, we perform an initial selection through a graph-guided method, followed by a fine-grained selection using domain adaptation method. Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset. ECAT enables full utilization of the entire space samples and representations under the supervision of the target task, while avoiding negative migration. Comprehensive experiments on real-world industrial datasets from Taobao show that ECAT advances state-of-the-art performance on offline metrics, and brings +13.6% CVR and +8.6% orders for Baiyibutie, a famous mini-app of Taobao. | [
"['Chaoqun Hou' 'Yuanhang Zhou' 'Yi Cao' 'Tong Liu']"
] |
null | null | 2407.02543 | null | null | http://arxiv.org/pdf/2407.02543v1 | 2024-07-02T07:13:35Z | 2024-07-02T07:13:35Z | Towards the Next Frontier in Speech Representation Learning Using
Disentanglement | The popular frameworks for self-supervised learning of speech representations have largely focused on frame-level masked prediction of speech regions. While this has shown promising downstream task performance for speech recognition and related tasks, this has largely ignored factors of speech that are encoded at coarser level, like characteristics of the speaker or channel that remain consistent through-out a speech utterance. In this work, we propose a framework for Learning Disentangled Self Supervised (termed as Learn2Diss) representations of speech, which consists of frame-level and an utterance-level encoder modules. The two encoders are initially learned independently, where the frame-level model is largely inspired by existing self supervision techniques, thereby learning pseudo-phonemic representations, while the utterance-level encoder is inspired by constrastive learning of pooled embeddings, thereby learning pseudo-speaker representations. The joint learning of these two modules consists of disentangling the two encoders using a mutual information based criterion. With several downstream evaluation experiments, we show that the proposed Learn2Diss achieves state-of-the-art results on a variety of tasks, with the frame-level encoder representations improving semantic tasks, while the utterance-level representations improve non-semantic tasks. | [
"['Varun Krishna' 'Sriram Ganapathy']"
] |
null | null | 2407.02546 | null | null | http://arxiv.org/pdf/2407.02546v1 | 2024-07-02T13:08:01Z | 2024-07-02T13:08:01Z | Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors | In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles. | [
"['Dinesh Cyril Selvaraj' 'Christian Vitale' 'Tania Panayiotou'\n 'Panayiotis Kolios' 'Carla Fabiana Chiasserini' 'Georgios Ellinas']"
] |
null | null | 2407.02547 | null | null | http://arxiv.org/pdf/2407.02547v1 | 2024-07-02T13:13:44Z | 2024-07-02T13:13:44Z | Domain Generalizable Knowledge Tracing via Concept Aggregation and
Relation-Based Attention | Knowledge Tracing (KT) is a critical task in online education systems, aiming to monitor students' knowledge states throughout a learning period. Common KT approaches involve predicting the probability of a student correctly answering the next question based on their exercise history. However, these methods often suffer from performance degradation when faced with the scarcity of student interactions in new education systems. To address this, we leverage student interactions from existing education systems to mitigate performance degradation caused by limited training data. Nevertheless, these interactions exhibit significant differences since they are derived from different education systems. To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains. Additionally, we design a domain-generalizable knowledge tracing framework (DGKT) that can be applied to any KT model. Specifically, we present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains. To further mitigate domain discrepancies, we introduce a novel normalization module called Sequence Instance Normalization (SeqIN). Moreover, to fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-Generalizable Relation-based Knowledge Tracing (DGRKT). Extensive experiments across five benchmark datasets demonstrate that the proposed method performs well despite limited training data. | [
"['Yuquan Xie' 'Wanqi Yang' 'Jinyu Wei' 'Ming Yang' 'Yang Gao']"
] |
null | null | 2407.02549 | null | null | http://arxiv.org/pdf/2407.02549v1 | 2024-07-02T15:27:06Z | 2024-07-02T15:27:06Z | Diffusion Models for Tabular Data Imputation and Synthetic Data
Generation | Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series data. Recently, they have been also adapted to generate tabular data. In this paper, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model's ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques, such as Variational Autoencoders, Generative Adversarial Networks and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) Machine Learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features. | [
"['Mario Villaizán-Vallelado' 'Matteo Salvatori' 'Carlos Segura'\n 'Ioannis Arapakis']"
] |
null | null | 2407.02552 | null | null | http://arxiv.org/pdf/2407.02552v1 | 2024-07-02T17:42:30Z | 2024-07-02T17:42:30Z | RLHF Can Speak Many Languages: Unlocking Multilingual Preference
Optimization for LLMs | Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs). However, despite widespread adoption, the vast majority of work to-date has focused on first-class citizen languages like English and Chinese. This captures a small fraction of the languages in the world, but also makes it unclear which aspects of current state-of-the-art research transfer to a multilingual setting. In this work, we perform an exhaustive study to achieve a new state-of-the-art in aligning multilingual LLMs. We introduce a novel, scalable method for generating high-quality multilingual feedback data to balance data coverage. We establish the benefits of cross-lingual transfer and increased dataset size in preference training. Our preference-trained model achieves a 54.4% win-rate against Aya 23 8B, the current state-of-the-art multilingual LLM in its parameter class, and a 69.5% win-rate or higher against widely used models like Gemma-1.1-7B-it, Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3. As a result of our study, we expand the frontier of alignment techniques to 23 languages covering half of the world's population. | [
"['John Dang' 'Arash Ahmadian' 'Kelly Marchisio' 'Julia Kreutzer'\n 'Ahmet Üstün' 'Sara Hooker']"
] |
null | null | 2407.02596 | null | null | http://arxiv.org/pdf/2407.02596v1 | 2024-07-02T18:33:49Z | 2024-07-02T18:33:49Z | Towards More Realistic Extraction Attacks: An Adversarial Perspective | Language models are prone to memorizing large parts of their training data, making them vulnerable to extraction attacks. Existing research on these attacks remains limited in scope, often studying isolated trends rather than the real-world interactions with these models. In this paper, we revisit extraction attacks from an adversarial perspective, exploiting the brittleness of language models. We find significant churn in extraction attack trends, i.e., even minor, unintuitive changes to the prompt, or targeting smaller models and older checkpoints, can exacerbate the risks of extraction by up to $2-4 times$. Moreover, relying solely on the widely accepted verbatim match underestimates the extent of extracted information, and we provide various alternatives to more accurately capture the true risks of extraction. We conclude our discussion with data deduplication, a commonly suggested mitigation strategy, and find that while it addresses some memorization concerns, it remains vulnerable to the same escalation of extraction risks against a real-world adversary. Our findings highlight the necessity of acknowledging an adversary's true capabilities to avoid underestimating extraction risks. | [
"['Yash More' 'Prakhar Ganesh' 'Golnoosh Farnadi']"
] |
null | null | 2407.02599 | null | null | http://arxiv.org/pdf/2407.02599v1 | 2024-07-02T18:37:52Z | 2024-07-02T18:37:52Z | Meta 3D Gen | We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based rendering (PBR), necessary for 3D asset relighting in real-world applications. Additionally, 3DGen supports generative retexturing of previously generated (or artist-created) 3D shapes using additional textual inputs provided by the user. 3DGen integrates key technical components, Meta 3D AssetGen and Meta 3D TextureGen, that we developed for text-to-3D and text-to-texture generation, respectively. By combining their strengths, 3DGen represents 3D objects simultaneously in three ways: in view space, in volumetric space, and in UV (or texture) space. The integration of these two techniques achieves a win rate of 68% with respect to the single-stage model. We compare 3DGen to numerous industry baselines, and show that it outperforms them in terms of prompt fidelity and visual quality for complex textual prompts, while being significantly faster. | [
"['Raphael Bensadoun' 'Tom Monnier' 'Yanir Kleiman' 'Filippos Kokkinos'\n 'Yawar Siddiqui' 'Mahendra Kariya' 'Omri Harosh' 'Roman Shapovalov'\n 'Benjamin Graham' 'Emilien Garreau' 'Animesh Karnewar' 'Ang Cao'\n 'Idan Azuri' 'Iurii Makarov' 'Eric-Tuan Le' 'Antoine Toisoul'\n 'David Novotny' 'Oran Gafni' 'Natalia Neverova' 'Andrea Vedaldi']"
] |
null | null | 2407.02601 | null | null | http://arxiv.org/pdf/2407.02601v1 | 2024-07-02T18:40:52Z | 2024-07-02T18:40:52Z | Linear Submodular Maximization with Bandit Feedback | Submodular optimization with bandit feedback has recently been studied in a variety of contexts. In a number of real-world applications such as diversified recommender systems and data summarization, the submodular function exhibits additional linear structure. We consider developing approximation algorithms for the maximization of a submodular objective function $f:2^Utomathbb{R}_{geq 0}$, where $f=sum_{i=1}^dw_iF_{i}$. It is assumed that we have value oracle access to the functions $F_i$, but the coefficients $w_i$ are unknown, and $f$ can only be accessed via noisy queries. We develop algorithms for this setting inspired by adaptive allocation algorithms in the best-arm identification for linear bandit, with approximation guarantees arbitrarily close to the setting where we have value oracle access to $f$. Finally, we empirically demonstrate that our algorithms make vast improvements in terms of sample efficiency compared to algorithms that do not exploit the linear structure of $f$ on instances of move recommendation. | [
"['Wenjing Chen' 'Victoria G. Crawford']"
] |
null | null | 2407.02604 | null | null | http://arxiv.org/pdf/2407.02604v1 | 2024-07-02T18:43:10Z | 2024-07-02T18:43:10Z | D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data
and eXpert model predictions | Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assistant for biomedicine, can perform multi-modal biomedical image and data analysis to provide a natural language interface for radiologists. While it is highly generalizable and works with multi-modal data, it is currently limited by well-known challenges that exist in the large language model space. Hallucinations and imprecision in responses can lead to misdiagnosis which currently hinder the clinical adaptability of VLMs. To create precise, user-friendly models in healthcare, we propose D-Rax -- a domain-specific, conversational, radiologic assistance tool that can be used to gain insights about a particular radiologic image. In this study, we enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting, offering comprehensive insights from medical imaging and aiding in the formulation of accurate diagnosis. D-Rax is achieved by fine-tuning the LLaVA-Med architecture on our curated enhanced instruction-following data, comprising of images, instructions, as well as disease diagnosis and demographic predictions derived from MIMIC-CXR imaging data, CXR-related visual question answer (VQA) pairs, and predictive outcomes from multiple expert AI models. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations. Leveraging the power of state-of-the-art diagnostic models combined with VLMs, D-Rax empowers clinicians to interact with medical images using natural language, which could potentially streamline their decision-making process, enhance diagnostic accuracy, and conserve their time. | [
"['Hareem Nisar' 'Syed Muhammad Anwar' 'Zhifan Jiang' 'Abhijeet Parida'\n 'Vishwesh Nath' 'Holger R. Roth' 'Marius George Linguraru']"
] |
null | null | 2407.02607 | null | null | http://arxiv.org/pdf/2407.02607v1 | 2024-07-02T18:46:13Z | 2024-07-02T18:46:13Z | Product Geometries on Cholesky Manifolds with Applications to SPD
Manifolds | This paper presents two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold, i.e., the space of lower triangular matrices with positive diagonal elements. We first unveil that the existing popular Riemannian metric on the Cholesky manifold can be generally characterized as the product metric of a Euclidean metric and a Riemannian metric on the space of n-dimensional positive vectors. Based on this analysis, we propose two novel metrics on the Cholesky manifolds, i.e., Diagonal Power Euclidean Metric and Diagonal Generalized Bures-Wasserstein Metric, which are numerically stabler than the existing Cholesky metric. We also discuss the gyro structures and deformed metrics associated with our metrics. The gyro structures connect the linear and geometric properties, while the deformed metrics interpolate between our proposed metrics and the existing metric. Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds. Compared with existing Riemannian metrics on SPD manifolds, our metrics are easy to use, computationally efficient, and numerically stable. | [
"['Ziheng Chen' 'Yue Song' 'Xiao-Jun Wu' 'Nicu Sebe']"
] |
null | null | 2407.02610 | null | null | http://arxiv.org/pdf/2407.02610v1 | 2024-07-02T18:55:58Z | 2024-07-02T18:55:58Z | Towards Federated Learning with On-device Training and Communication in
8-bit Floating Point | Recent work has shown that 8-bit floating point (FP8) can be used for efficiently training neural networks with reduced computational overhead compared to training in FP32/FP16. In this work, we investigate the use of FP8 training in a federated learning context. This brings not only the usual benefits of FP8 which are desirable for on-device training at the edge, but also reduces client-server communication costs due to significant weight compression. We present a novel method for combining FP8 client training while maintaining a global FP32 server model and provide convergence analysis. Experiments with various machine learning models and datasets show that our method consistently yields communication reductions of at least 2.9x across a variety of tasks and models compared to an FP32 baseline. | [
"['Bokun Wang' 'Axel Berg' 'Durmus Alp Emre Acar' 'Chuteng Zhou']"
] |
null | null | 2407.02625 | null | null | http://arxiv.org/pdf/2407.02625v1 | 2024-07-02T19:30:25Z | 2024-07-02T19:30:25Z | Lung-CADex: Fully automatic Zero-Shot Detection and Classification of
Lung Nodules in Thoracic CT Images | Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. Large Visual Language models (VLMs) have been found effective for multiple downstream medical tasks that rely on both imaging and text data. However, lesion level detection and subsequent diagnosis using VLMs have not been explored yet. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. CADe trains on a prompt suite on input computed tomography (CT) scans by using the CLIP text encoder through prefix tuning. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning. Training and validation of CADe and CADx have been done using one of the largest publicly available datasets, called LIDC. To check the generalization ability of the model, it is also evaluated on a challenging dataset, LUNGx. Our experimental results show that the proposed methods achieve a sensitivity of 0.86 compared to 0.76 that of other fully supervised methods.The source code, datasets and pre-processed data can be accessed using the link: | [
"['Furqan Shaukat' 'Syed Muhammad Anwar' 'Abhijeet Parida' 'Van Khanh Lam'\n 'Marius George Linguraru' 'Mubarak Shah']"
] |
null | null | 2407.02641 | null | null | http://arxiv.org/pdf/2407.02641v1 | 2024-07-02T20:14:32Z | 2024-07-02T20:14:32Z | Learning Graph Structures and Uncertainty for Accurate and Calibrated
Time-series Forecasting | Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks. | [
"['Harshavardhan Kamarthi' 'Lingkai Kong' 'Alexander Rodriguez'\n 'Chao Zhang' 'B Aditya Prakash']"
] |
null | null | 2407.02653 | null | null | http://arxiv.org/pdf/2407.02653v1 | 2024-07-02T20:35:58Z | 2024-07-02T20:35:58Z | Joint Segmentation and Image Reconstruction with Error Prediction in
Photoacoustic Imaging using Deep Learning | Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level. | [
"['Ruibo Shang' 'Geoffrey P. Luke' \"Matthew O'Donnell\"]"
] |
null | null | 2407.02657 | null | null | http://arxiv.org/pdf/2407.02657v1 | 2024-07-02T20:40:08Z | 2024-07-02T20:40:08Z | Large Scale Hierarchical Industrial Demand Time-Series Forecasting
incorporating Sparsity | Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience. | [
"['Harshavardhan Kamarthi' 'Aditya B. Sasanur' 'Xinjie Tong' 'Xingyu Zhou'\n 'James Peters' 'Joe Czyzyk' 'B. Aditya Prakash']"
] |
null | null | 2407.02659 | null | null | http://arxiv.org/pdf/2407.02659v1 | 2024-07-02T20:49:21Z | 2024-07-02T20:49:21Z | Ensuring Responsible Sourcing of Large Language Model Training Data
Through Knowledge Graph Comparison | In light of recent plagiarism allegations Brough by publishers, newspapers, and other creators of copyrighted corpora against large language model (LLM) developers, we propose a novel system, a variant of a plagiarism detection system, that assesses whether a knowledge source has been used in the training or fine-tuning of a large language model. Unlike current methods, we utilize an approach that uses Resource Description Framework (RDF) triples to create knowledge graphs from both a source document and a LLM continuation of that document. These graphs are then analyzed with respect to content using cosine similarity and with respect to structure using a normalized version of graph edit distance that shows the degree of isomorphism. Unlike traditional systems that focus on content matching and keyword identification between a source and target corpus, our approach enables a broader evaluation of similarity and thus a more accurate comparison of the similarity between a source document and LLM continuation by focusing on relationships between ideas and their organization with regards to others. Additionally, our approach does not require access to LLM metrics like perplexity that may be unavailable in closed large language modeling "black-box" systems, as well as the training corpus. A prototype of our system will be found on a hyperlinked GitHub repository. | [
"['Devam Mondal' 'Carlo Lipizzi']"
] |
null | null | 2407.02681 | null | null | http://arxiv.org/pdf/2407.02681v1 | 2024-07-02T21:46:23Z | 2024-07-02T21:46:23Z | Uniform Transformation: Refining Latent Representation in Variational
Autoencoders | Irregular distribution in latent space causes posterior collapse, misalignment between posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this paper, we introduce a novel adaptable three-stage Uniform Transformation (UT) module -- Gaussian Kernel Density Estimation (G-KDE) clustering, non-parametric Gaussian Mixture (GM) Modeling, and Probability Integral Transform (PIT) -- to address irregular latent distributions. By reconfiguring irregular distributions into a uniform distribution in the latent space, our approach significantly enhances the disentanglement and interpretability of latent representations, overcoming the limitation of traditional VAE models in capturing complex data structures. Empirical evaluations demonstrated the efficacy of our proposed UT module in improving disentanglement metrics across benchmark datasets -- dSprites and MNIST. Our findings suggest a promising direction for advancing representation learning techniques, with implication for future research in extending this framework to more sophisticated datasets and downstream tasks. | [
"['Ye Shi' 'C. S. George Lee']"
] |
null | null | 2407.02687 | null | null | http://arxiv.org/pdf/2407.02687v1 | 2024-07-02T22:04:00Z | 2024-07-02T22:04:00Z | No Training, No Problem: Rethinking Classifier-Free Guidance for
Diffusion Models | Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information. | [
"['Seyedmorteza Sadat' 'Manuel Kansy' 'Otmar Hilliges' 'Romann M. Weber']"
] |
null | null | 2407.02689 | null | null | http://arxiv.org/pdf/2407.02689v1 | 2024-07-02T22:14:54Z | 2024-07-02T22:14:54Z | Accelerating Distributed Optimization: A Primal-Dual Perspective on
Local Steps | In distributed machine learning, efficient training across multiple agents with different data distributions poses significant challenges. Even with a centralized coordinator, current algorithms that achieve optimal communication complexity typically require either large minibatches or compromise on gradient complexity. In this work, we tackle both centralized and decentralized settings across strongly convex, convex, and nonconvex objectives. We first demonstrate that a basic primal-dual method, (Accelerated) Gradient Ascent Multiple Stochastic Gradient Descent (GA-MSGD), applied to the Lagrangian of distributed optimization inherently incorporates local updates, because the inner loops of running Stochastic Gradient Descent on the primal variable require no inter-agent communication. Notably, for strongly convex objectives, we show (Accelerated) GA-MSGD achieves linear convergence in communication rounds despite the Lagrangian being only linear in the dual variables. This is due to a unique structural property where the dual variable is confined to the span of the coupling matrix, rendering the dual problem strongly concave. When integrated with the Catalyst framework, our approach achieves nearly optimal communication complexity across various settings without the need for minibatches. Moreover, in stochastic decentralized problems, it attains communication complexities comparable to those in deterministic settings, improving over existing algorithms. | [
"['Junchi Yang' 'Murat Yildirim' 'Qiu Feng']"
] |
null | null | 2407.02693 | null | null | http://arxiv.org/abs/2407.02693v1 | 2024-07-02T22:21:03Z | 2024-07-02T22:21:03Z | UAV-assisted Distributed Learning for Environmental Monitoring in Rural
Environments | Distributed learning and inference algorithms have become indispensable for IoT systems, offering benefits such as workload alleviation, data privacy preservation, and reduced latency. This paper introduces an innovative approach that utilizes unmanned aerial vehicles (UAVs) as a coverage extension relay for IoT environmental monitoring in rural areas. Our method integrates a split learning (SL) strategy between edge devices, a UAV and a server to enhance adaptability and performance of inference mechanisms. By employing UAVs as a relay and by incorporating SL, we address connectivity and resource constraints for applications of learning in IoT in remote settings. Our system model accounts for diverse channel conditions to determine the most suitable transmission strategy for optimal system behaviour. Through simulation analysis, the proposed approach demonstrates its robustness and adaptability, even excelling under adverse channel conditions. Integrating UAV relaying and the SL paradigm offers significant flexibility to the server, enabling adaptive strategies that consider various trade-offs beyond simply minimizing overall inference quality. | [
"['Vukan Ninkovic' 'Dejan Vukobratovic' 'Dragisa Miskovic']"
] |
null | null | 2407.02694 | null | null | http://arxiv.org/pdf/2407.02694v1 | 2024-07-02T22:23:40Z | 2024-07-02T22:23:40Z | LLM-Select: Feature Selection with Large Language Models | In this paper, we demonstrate a surprising capability of large language models (LLMs): given only input feature names and a description of a prediction task, they are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Remarkably, these models exhibit this capacity across various query mechanisms. For example, we zero-shot prompt an LLM to output a numerical importance score for a feature (e.g., "blood pressure") in predicting an outcome of interest (e.g., "heart failure"), with no additional context. In particular, we find that the latest models, such as GPT-4, can consistently identify the most predictive features regardless of the query mechanism and across various prompting strategies. We illustrate these findings through extensive experiments on real-world data, where we show that LLM-based feature selection consistently achieves strong performance competitive with data-driven methods such as the LASSO, despite never having looked at the downstream training data. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place. This could potentially benefit practitioners in domains like healthcare, where collecting high-quality data comes at a high cost. | [
"['Daniel P. Jeong' 'Zachary C. Lipton' 'Pradeep Ravikumar']"
] |
null | null | 2407.02700 | null | null | http://arxiv.org/pdf/2407.02700v1 | 2024-07-02T22:47:40Z | 2024-07-02T22:47:40Z | Output Range Analysis for Deep Neural Networks based on Simulated
Annealing Processes | This paper tackles the challenging problem of output range estimation for Deep Neural Networks (DNNs), introducing a novel algorithm based on Simulated Annealing (SA). Our approach addresses the lack of local geometric information and high non-linearity in DNNs, making it versatile across various architectures, especially Residual Neural Networks (ResNets). We present a straightforward, implementation-friendly algorithm that avoids restrictive assumptions about network architecture. Through theoretical analysis and experimental evaluations, including tests on the Ackley function, we demonstrate our algorithm's effectiveness in navigating complex, non-convex surfaces and accurately estimating DNN output ranges. Futhermore, the Python codes of this experimental evaluation that support our results are available in our GitHub repository (https://github.com/Nicerova7/output-range-analysis-for-deep-neural-networks-with-simulated-annealing). | [
"['Helder Rojas' 'Nilton Rojas' 'Espinoza J. B.' 'Luis Huamanchumo']"
] |
null | null | 2407.02702 | null | null | http://arxiv.org/pdf/2407.02702v2 | 2024-07-10T04:58:42Z | 2024-07-02T22:51:01Z | Practical Guide for Causal Pathways and Sub-group Disparity Analysis | In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves employing causal decomposition analysis to quantify and examine the causal interplay between sensitive attributes and outcomes. We also emphasize the significance of integrating heterogeneity assessment in causal disparity analysis to gain deeper insights into the impact of sensitive attributes within specific sub-groups on outcomes. Our two-step investigation focuses on datasets where race serves as the sensitive attribute. The results on two datasets indicate the benefit of leveraging causal analysis and heterogeneity assessment not only for quantifying biases in the data but also for disentangling their influences on outcomes. We demonstrate that the sub-groups identified by our approach to be affected the most by disparities are the ones with the largest ML classification errors. We also show that grouping the data only based on a sensitive attribute is not enough, and through these analyses, we can find sub-groups that are directly affected by disparities. We hope that our findings will encourage the adoption of such methodologies in future ethical AI practices and bias audits, fostering a more equitable and fair technological landscape. | [
"['Farnaz Kohankhaki' 'Shaina Raza' 'Oluwanifemi Bamgbose' 'Deval Pandya'\n 'Elham Dolatabadi']"
] |
null | null | 2407.02713 | null | null | http://arxiv.org/pdf/2407.02713v1 | 2024-07-02T23:30:01Z | 2024-07-02T23:30:01Z | Advancing Compressed Video Action Recognition through Progressive
Knowledge Distillation | Compressed video action recognition classifies video samples by leveraging the different modalities in compressed videos, namely motion vectors, residuals, and intra-frames. For this purpose, three neural networks are deployed, each dedicated to processing one modality. Our observations indicate that the network processing intra-frames tend to converge to a flatter minimum than the network processing residuals, which in turn converges to a flatter minimum than the motion vector network. This hierarchy in convergence motivates our strategy for knowledge transfer among modalities to achieve flatter minima, which are generally associated with better generalization. With this insight, we propose Progressive Knowledge Distillation (PKD), a technique that incrementally transfers knowledge across the modalities. This method involves attaching early exits (Internal Classifiers - ICs) to the three networks. PKD distills knowledge starting from the motion vector network, followed by the residual, and finally, the intra-frame network, sequentially improving IC accuracy. Further, we propose the Weighted Inference with Scaled Ensemble (WISE), which combines outputs from the ICs using learned weights, boosting accuracy during inference. Our experiments demonstrate the effectiveness of training the ICs with PKD compared to standard cross-entropy-based training, showing IC accuracy improvements of up to 5.87% and 11.42% on the UCF-101 and HMDB-51 datasets, respectively. Additionally, WISE improves accuracy by up to 4.28% and 9.30% on UCF-101 and HMDB-51, respectively. | [
"['Efstathia Soufleri' 'Deepak Ravikumar' 'Kaushik Roy']"
] |
null | null | 2407.02716 | null | null | http://arxiv.org/pdf/2407.02716v1 | 2024-07-02T23:48:43Z | 2024-07-02T23:48:43Z | Light-weight Fine-tuning Method for Defending Adversarial Noise in
Pre-trained Medical Vision-Language Models | Fine-tuning pre-trained Vision-Language Models (VLMs) has shown remarkable capabilities in medical image and textual depiction synergy. Nevertheless, many pre-training datasets are restricted by patient privacy concerns, potentially containing noise that can adversely affect downstream performance. Moreover, the growing reliance on multi-modal generation exacerbates this issue because of its susceptibility to adversarial attacks. To investigate how VLMs trained on adversarial noisy data perform on downstream medical tasks, we first craft noisy upstream datasets using multi-modal adversarial attacks. Through our comprehensive analysis, we unveil that moderate noise enhances model robustness and transferability, but increasing noise levels negatively impact downstream task performance. To mitigate this issue, we propose rectify adversarial noise (RAN) framework, a recipe designed to effectively defend adversarial attacks and rectify the influence of upstream noise during fine-tuning. | [
"['Xu Han' 'Linghao Jin' 'Xuezhe Ma' 'Xiaofeng Liu']"
] |
null | null | 2407.02721 | null | null | http://arxiv.org/pdf/2407.02721v1 | 2024-07-03T00:25:25Z | 2024-07-03T00:25:25Z | Model and Feature Diversity for Bayesian Neural Networks in Mutual
Learning | Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature distributions, promoting peer networks to acquire distinct features that capture different characteristics of the input, which enhances the effectiveness of mutual learning. Experimental results demonstrate significant improvements in the classification accuracy, negative log-likelihood, and expected calibration error when compared to traditional mutual learning for BNNs. | [
"['Cuong Pham' 'Cuong C. Nguyen' 'Trung Le' 'Dinh Phung' 'Gustavo Carneiro'\n 'Thanh-Toan Do']"
] |
null | null | 2407.02737 | null | null | http://arxiv.org/pdf/2407.02737v1 | 2024-07-03T01:20:26Z | 2024-07-03T01:20:26Z | Development of Machine Learning Classifiers for Blood-based Diagnosis
and Prognosis of Suspected Acute Infections and Sepsis | We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned. | [
"['Ljubomir Buturovic' 'Michael Mayhew' 'Roland Luethy' 'Kirindi Choi'\n 'Uros Midic' 'Nandita Damaraju' 'Yehudit Hasin-Brumshtein'\n 'Amitesh Pratap' 'Rhys M. Adams' 'Joao Fonseca' 'Ambika Srinath'\n 'Paul Fleming' 'Claudia Pereira' 'Oliver Liesenfeld' 'Purvesh Khatri'\n 'Timothy Sweeney']"
] |
null | null | 2407.02747 | null | null | http://arxiv.org/pdf/2407.02747v1 | 2024-07-03T01:47:46Z | 2024-07-03T01:47:46Z | Curvature Clues: Decoding Deep Learning Privacy with Input Loss
Curvature | In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature) is the trace of the Hessian of the loss with respect to the input. We investigate how input loss curvature varies between train and test sets, and its implications for train-test distinguishability. We develop a theoretical framework that derives an upper bound on the train-test distinguishability based on privacy and the size of the training set. This novel insight fuels the development of a new black box membership inference attack utilizing input loss curvature. We validate our theoretical findings through experiments in computer vision classification tasks, demonstrating that input loss curvature surpasses existing methods in membership inference effectiveness. Our analysis highlights how the performance of membership inference attack (MIA) methods varies with the size of the training set, showing that curvature-based MIA outperforms other methods on sufficiently large datasets. This condition is often met by real datasets, as demonstrated by our results on CIFAR10, CIFAR100, and ImageNet. These findings not only advance our understanding of deep neural network behavior but also improve the ability to test privacy-preserving techniques in machine learning. | [
"['Deepak Ravikumar' 'Efstathia Soufleri' 'Kaushik Roy']"
] |
null | null | 2407.02758 | null | null | http://arxiv.org/pdf/2407.02758v1 | 2024-07-03T02:23:33Z | 2024-07-03T02:23:33Z | Differential Encoding for Improved Representation Learning over Graphs | Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the global attention mechanism fundamentally generate node embeddings based on information aggregated from a node's local neighborhood or from the whole graph. The most basic and commonly used aggregation approach is to take the sum of information from a node's local neighbourhood or from the whole graph. However, it is unknown if the dominant information is from a node itself or from the node's neighbours (or the rest of the graph nodes). Therefore, there exists information lost at each layer of embedding generation, and this information lost could be accumulated and become more serious when more layers are used in the model. In this paper, we present a differential encoding method to address the issue of information lost. The idea of our method is to encode the differential representation between the information from a node's neighbours (or the rest of the graph nodes) and that from the node itself. The obtained differential encoding is then combined with the original aggregated local or global representation to generate the updated node embedding. By integrating differential encodings, the representational ability of generated node embeddings is improved. The differential encoding method is empirically evaluated on different graph tasks on seven benchmark datasets. The results show that it is a general method that improves the message-passing update and the global attention update, advancing the state-of-the-art performance for graph representation learning on these datasets. | [
"['Haimin Zhang' 'Jiahao Xia' 'Min Xu']"
] |
null | null | 2407.02759 | null | null | http://arxiv.org/pdf/2407.02759v1 | 2024-07-03T02:33:20Z | 2024-07-03T02:33:20Z | Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning
to Optimize the Advertising Recommendation System | This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings. | [
"['Yang Zhao' 'Chang Zhou' 'Jin Cao' 'Yi Zhao' 'Shaobo Liu' 'Chiyu Cheng'\n 'Xingchen Li']"
] |
null | null | 2407.02762 | null | null | http://arxiv.org/pdf/2407.02762v1 | 2024-07-03T02:40:39Z | 2024-07-03T02:40:39Z | SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph
Neural Network | Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as homogeneous graphs, heterogeneous graphs, and more complex graphs like knowledge graphs. However, merely stacking GNN layers may not improve the model's performance and can even be detrimental. For the phenomenon of performance degradation in deep GNNs, we propose a new perspective. Unlike the popular explanations of over-smoothing or over-squashing, we think the issue arises from the interference of low-quality node representations during message propagation. We introduce a simple and general method, SF-GNN, to address this problem. In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes. A self-filter module evaluates the quality of the node representation and decides whether to integrate it into the message propagation based on this quality assessment. Experiments on node classification tasks for both homogeneous and heterogeneous graphs, as well as link prediction tasks on knowledge graphs, demonstrate that our method can be applied to various GNN models and outperforms state-of-the-art baseline methods in addressing deep GNN degradation. | [
"['Yushan Zhu' 'Wen Zhang' 'Yajing Xu' 'Zhen Yao' 'Mingyang Chen'\n 'Huajun Chen']"
] |
null | null | 2407.02770 | null | null | http://arxiv.org/pdf/2407.02770v1 | 2024-07-03T02:57:40Z | 2024-07-03T02:57:40Z | Large language models, physics-based modeling, experimental
measurements: the trinity of data-scarce learning of polymer properties | Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessitates a wealth of data to achieve accuracy and mitigate overfitting. However, experimental measurements are often limited and costly to obtain in sufficient quantities for finetuning. To this end, we present a physics-based training pipeline that tackles the pathology of data scarcity. The core enabler is a physics-based modeling framework that generates a multitude of synthetic data to align the LLM to a physically consistent initial state before finetuning. Our framework features a two-phase training strategy: (1) utilizing the large-in-amount while less accurate synthetic data for supervised pretraining, and (2) finetuning the phase-1 model with limited experimental data. We empirically demonstrate that supervised pretraining is vital to obtaining accurate finetuned LLMs, via the lens of learning polymer flammability metrics where cone calorimeter data is sparse. | [
"['Ning Liu' 'Siavash Jafarzadeh' 'Brian Y. Lattimer' 'Shuna Ni' 'Jim Lua'\n 'Yue Yu']"
] |
null | null | 2407.02772 | null | null | http://arxiv.org/pdf/2407.02772v1 | 2024-07-03T03:01:43Z | 2024-07-03T03:01:43Z | Automatic gradient descent with generalized Newton's method | We propose the generalized Newton's method (GeN) -- a Hessian-informed approach that applies to any optimizer such as SGD and Adam, and covers the Newton-Raphson method as a sub-case. Our method automatically and dynamically selects the learning rate that accelerates the convergence, without the intensive tuning of the learning rate scheduler. In practice, out method is easily implementable, since it only requires additional forward passes with almost zero computational overhead (in terms of training time and memory cost), if the overhead is amortized over many iterations. We present extensive experiments on language and vision tasks (e.g. GPT and ResNet) to showcase that GeN optimizers match the state-of-the-art performance, which was achieved with carefully tuned learning rate schedulers. Code to be released at url{https://github.com/ShiyunXu/AutoGeN}. | [
"['Zhiqi Bu' 'Shiyun Xu']"
] |
null | null | 2407.02775 | null | null | http://arxiv.org/pdf/2407.02775v1 | 2024-07-03T03:03:30Z | 2024-07-03T03:03:30Z | MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language
Models | Knowledge distillation is an effective technique for pre-trained language model compression. Although existing knowledge distillation methods perform well for the most typical model BERT, they could be further improved in two aspects: the relation-level knowledge could be further explored to improve model performance; and the setting of student attention head number could be more flexible to decrease inference time. Therefore, we are motivated to propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. Extensive experiments on GLUE benchmark and extractive question answering tasks demonstrate that our method outperforms state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT can flexibly set student attention head number, allowing for substantial inference time decrease with little performance drop. | [
"['Ying Zhang' 'Ziheng Yang' 'Shufan Ji']"
] |
null | null | 2407.02778 | null | null | http://arxiv.org/pdf/2407.02778v1 | 2024-07-03T03:10:24Z | 2024-07-03T03:10:24Z | Foster Adaptivity and Balance in Learning with Noisy Labels | Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised models due to its effect in hurting the generalization performance of deep neural networks. Existing methods primarily employ the sample selection paradigm and usually rely on dataset-dependent prior knowledge (eg, a pre-defined threshold) to cope with label noise, inevitably degrading the adaptivity. Moreover, existing methods tend to neglect the class balance in selecting samples, leading to biased model performance. To this end, we propose a simple yet effective approach named textbf{SED} to deal with label noise in a textbf{S}elf-adaptivtextbf{E} and class-balancetextbf{D} manner. Specifically, we first design a novel sample selection strategy to empower self-adaptivity and class balance when identifying clean and noisy data. A mean-teacher model is then employed to correct labels of noisy samples. Subsequently, we propose a self-adaptive and class-balanced sample re-weighting mechanism to assign different weights to detected noisy samples. Finally, we additionally employ consistency regularization on selected clean samples to improve model generalization performance. Extensive experimental results on synthetic and real-world datasets demonstrate the effectiveness and superiority of our proposed method. The source code has been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SED. | [
"['Mengmeng Sheng' 'Zeren Sun' 'Tao Chen' 'Shuchao Pang' 'Yucheng Wang'\n 'Yazhou Yao']"
] |
null | null | 2407.02779 | null | null | http://arxiv.org/pdf/2407.02779v1 | 2024-07-03T03:10:25Z | 2024-07-03T03:10:25Z | Croppable Knowledge Graph Embedding | Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs) to serve various artificial intelligence tasks. The suitable dimensions of the embeddings depend on the storage and computing conditions of the specific application scenarios. Once a new dimension is required, a new KGE model needs to be trained from scratch, which greatly increases the training cost and limits the efficiency and flexibility of KGE in serving various scenarios. In this work, we propose a novel KGE training framework MED, through which we could train once to get a croppable KGE model applicable to multiple scenarios with different dimensional requirements, sub-models of the required dimensions can be cropped out of it and used directly without any additional training. In MED, we propose a mutual learning mechanism to improve the low-dimensional sub-models performance and make the high-dimensional sub-models retain the capacity that low-dimensional sub-models have, an evolutionary improvement mechanism to promote the high-dimensional sub-models to master the knowledge that the low-dimensional sub-models can not learn, and a dynamic loss weight to balance the multiple losses adaptively. Experiments on 3 KGE models over 4 standard KG completion datasets, 3 real application scenarios over a real-world large-scale KG, and the experiments of extending MED to the language model BERT show the effectiveness, high efficiency, and flexible extensibility of MED. | [
"['Yushan Zhu' 'Wen Zhang' 'Zhiqiang Liu' 'Mingyang Chen' 'Lei Liang'\n 'Huajun Chen']"
] |
null | null | 2407.02811 | null | null | http://arxiv.org/pdf/2407.02811v1 | 2024-07-03T05:13:28Z | 2024-07-03T05:13:28Z | SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing | Certifiable robustness gives the guarantee that small perturbations around an input to a classifier will not change the prediction. There are two approaches to provide certifiable robustness to adversarial examples: a) explicitly training classifiers with small Lipschitz constants, and b) Randomized smoothing, which adds random noise to the input to create a smooth classifier. We propose textit{SPLITZ}, a practical and novel approach which leverages the synergistic benefits of both the above ideas into a single framework. Our main idea is to textit{split} a classifier into two halves, constrain the Lipschitz constant of the first half, and smooth the second half via randomization. Motivation for textit{SPLITZ} comes from the observation that many standard deep networks exhibit heterogeneity in Lipschitz constants across layers. textit{SPLITZ} can exploit this heterogeneity while inheriting the scalability of randomized smoothing. We present a principled approach to train textit{SPLITZ} and provide theoretical analysis to derive certified robustness guarantees during inference. We present a comprehensive comparison of robustness-accuracy tradeoffs and show that textit{SPLITZ} consistently improves upon existing state-of-the-art approaches on MNIST and CIFAR-10 datasets. For instance, with $ell_2$ norm perturbation budget of textbf{$epsilon=1$}, textit{SPLITZ} achieves $textbf{43.2%}$ top-1 test accuracy on CIFAR-10 dataset compared to state-of-art top-1 test accuracy $textbf{39.8%} | [
"['Meiyu Zhong' 'Ravi Tandon']"
] |
null | null | 2407.02813 | null | null | http://arxiv.org/pdf/2407.02813v2 | 2024-07-12T03:39:05Z | 2024-07-03T05:17:26Z | Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm
and Compiler Co-Design | Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take advantage of DNN's overfitting properties to perform video resolution upscaling. By splitting videos into chunks and applying a super-resolution (SR) model to overfit each chunk, this scheme of SR models plus video chunks is able to replace traditional video transmission to enhance video quality and transmission efficiency. However, many models and chunks are needed to guarantee high performance, which leads to tremendous overhead on model switching and memory footprints at the user end. To resolve such problems, we propose a Dynamic Deep neural network assisted by a Content-Aware data processing pipeline to reduce the model number down to one (Dy-DCA), which helps promote performance while conserving computational resources. Additionally, to achieve real acceleration on the user end, we designed a framework that optimizes dynamic features (e.g., dynamic shapes, sizes, and control flow) in Dy-DCA to enable a series of compilation optimizations, including fused code generation, static execution planning, etc. By employing such techniques, our method achieves better PSNR and real-time performance (33 FPS) on an off-the-shelf mobile phone. Meanwhile, assisted by our compilation optimization, we achieve a 1.7$times$ speedup while saving up to 1.61$times$ memory consumption. Code available in https://github.com/coulsonlee/Dy-DCA-ECCV2024. | [
"['Gen Li' 'Zhihao Shu' 'Jie Ji' 'Minghai Qin' 'Fatemeh Afghah' 'Wei Niu'\n 'Xiaolong Ma']"
] |
null | null | 2407.02819 | null | null | http://arxiv.org/pdf/2407.02819v1 | 2024-07-03T05:40:41Z | 2024-07-03T05:40:41Z | Efficient Training of Language Models with Compact and Consistent Next
Token Distributions | Maximizing the likelihood of the next token is an established, statistically sound objective for pre-training language models. In this paper we show that we can train better models faster by pre-aggregating the corpus with a collapsed $n$-gram distribution. Previous studies have proposed corpus-level $n$-gram statistics as a regularizer; however, the construction and querying of such $n$-grams, if done naively, prove to be costly and significantly impede training speed, thereby limiting their application in modern large language model pre-training. We introduce an alternative compact representation of the next token distribution that, in expectation, aligns with the complete $n$-gram distribution while markedly reducing variance across mini-batches compared to the standard next-token loss. Empirically, we demonstrate that both the $n$-gram regularized model and our approximation yield substantial improvements in model quality and convergence rate compared to existing methods. Furthermore, our approximation facilitates scalability of gains to larger datasets and models compared to the straightforward $n$-gram regularization method. | [
"['Ashutosh Sathe' 'Sunita Sarawagi']"
] |
null | null | 2407.02821 | null | null | http://arxiv.org/pdf/2407.02821v1 | 2024-07-03T05:45:09Z | 2024-07-03T05:45:09Z | Effect of a Process Mining based Pre-processing Step in Prediction of
the Critical Health Outcomes | Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences that create complexities, leading to poor predictions. Consequently, pre-processing the data is crucial to improve its quality. In this study, we use an existing pre-processing algorithm, concatenation, to improve data quality by decreasing the complexity of datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC III and University of Illinois Hospital - converted to the event logs, they were then fed into the concatenation algorithm. The pre-processed event logs were then fed to the Split Miner (SM) algorithm to produce a process model. Process model quality was evaluated before and after concatenation using the following metrics: fitness, precision, F-Measure, and complexity. The pre-processed event logs were also used as inputs to the Decay Replay Mining (DREAM) algorithm to predict critical outcomes. We compared predicted results before and after applying the concatenation algorithm using Area Under the Curve (AUC) and Confidence Intervals (CI). Results indicated that the concatenation algorithm improved the quality of the process models and predictions of the critical health outcomes. | [
"['Negin Ashrafi' 'Armin Abdollahi' 'Greg Placencia' 'Maryam Pishgar']"
] |
null | null | 2407.02825 | null | null | http://arxiv.org/abs/2407.02825v1 | 2024-07-03T05:51:57Z | 2024-07-03T05:51:57Z | Representation learning with CGAN for casual inference | Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper proposes a new method for finding representation learning functions by adopting the adversarial idea. We apply the pattern of CGAN and theoretically emonstrate the feasibility of finding a suitable representation function in the context of two distributions being balanced. The theoretical result shows that when two distributions are balanced, the ideal representation function can be found and thus can be used to further research. | [
"['Zhaotian Weng' 'Jianbo Hong' 'Lan Wang']"
] |
null | null | 2407.02827 | null | null | http://arxiv.org/pdf/2407.02827v1 | 2024-07-03T06:10:41Z | 2024-07-03T06:10:41Z | Convergence of Implicit Gradient Descent for Training Two-Layer
Physics-Informed Neural Networks | Optimization algorithms is crucial in training physics-informed neural networks (PINNs), unsuitable methods may lead to poor solutions. Compared to the common gradient descent algorithm, implicit gradient descent (IGD) outperforms it in handling some multi-scale problems. In this paper, we provide convergence analysis for the implicit gradient descent for training over-parametrized two-layer PINNs. We first demonstrate the positive definiteness of Gram matrices for general smooth activation functions, like sigmoidal function, softplus function, tanh function and so on. Then the over-parameterization allows us to show that the randomly initialized IGD converges a globally optimal solution at a linear convergence rate. Moreover, due to the different training dynamics, the learning rate of IGD can be chosen independent of the sample size and the least eigenvalue of the Gram matrix. | [
"['Xianliang Xu' 'Zhongyi Huang' 'Ye Li']"
] |
null | null | 2407.02833 | null | null | http://arxiv.org/pdf/2407.02833v1 | 2024-07-03T06:20:31Z | 2024-07-03T06:20:31Z | LANE: Logic Alignment of Non-tuning Large Language Models and Online
Recommendation Systems for Explainable Reason Generation | The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation. However, in existing related studies, fine-tuning LLM models for recommendation tasks incurs high computational costs and alignment issues with existing systems, limiting the application potential of proven proprietary/closed-source LLM models, such as GPT-4. In this work, our proposed effective strategy LANE aligns LLMs with online recommendation systems without additional LLMs tuning, reducing costs and improving explainability. This innovative approach addresses key challenges in integrating language models with recommendation systems while fully utilizing the capabilities of powerful proprietary models. Specifically, our strategy operates through several key components: semantic embedding, user multi-preference extraction using zero-shot prompting, semantic alignment, and explainable recommendation generation using Chain of Thought (CoT) prompting. By embedding item titles instead of IDs and utilizing multi-head attention mechanisms, our approach aligns the semantic features of user preferences with those of candidate items, ensuring coherent and user-aligned recommendations. Sufficient experimental results including performance comparison, questionnaire voting, and visualization cases prove that our method can not only ensure recommendation performance, but also provide easy-to-understand and reasonable recommendation logic. | [
"['Hongke Zhao' 'Songming Zheng' 'Likang Wu' 'Bowen Yu' 'Jing Wang']"
] |
null | null | 2407.02844 | null | null | http://arxiv.org/pdf/2407.02844v3 | 2024-07-15T17:55:49Z | 2024-07-03T06:40:26Z | Multi-Attention Integrated Deep Learning Frameworks for Enhanced Breast
Cancer Segmentation and Identification | Breast cancer poses a profound threat to lives globally, claiming numerous lives each year. Therefore, timely detection is crucial for early intervention and improved chances of survival. Accurately diagnosing and classifying breast tumors using ultrasound images is a persistent challenge in medicine, demanding cutting-edge solutions for improved treatment strategies. This research introduces multiattention-enhanced deep learning (DL) frameworks designed for the classification and segmentation of breast cancer tumors from ultrasound images. A spatial channel attention mechanism is proposed for segmenting tumors from ultrasound images, utilizing a novel LinkNet DL framework with an InceptionResNet backbone. Following this, the paper proposes a deep convolutional neural network with an integrated multi-attention framework (DCNNIMAF) to classify the segmented tumor as benign, malignant, or normal. From experimental results, it is observed that the segmentation model has recorded an accuracy of 98.1%, with a minimal loss of 0.6%. It has also achieved high Intersection over Union (IoU) and Dice Coefficient scores of 96.9% and 97.2%, respectively. Similarly, the classification model has attained an accuracy of 99.2%, with a low loss of 0.31%. Furthermore, the classification framework has achieved outstanding F1-Score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively. By offering a robust framework for early detection and accurate classification of breast cancer, this proposed work significantly advances the field of medical image analysis, potentially improving diagnostic precision and patient outcomes. | [
"['Pandiyaraju V' 'Shravan Venkatraman' 'Pavan Kumar S'\n 'Santhosh Malarvannan' 'Kannan A']"
] |
null | null | 2407.02855 | null | null | http://arxiv.org/pdf/2407.02855v1 | 2024-07-03T07:14:05Z | 2024-07-03T07:14:05Z | Safe Unlearning: A Surprisingly Effective and Generalizable Solution to
Defend Against Jailbreak Attacks | LLMs are known to be vulnerable to jailbreak attacks, even after safety alignment. An important observation is that, while different types of jailbreak attacks can generate significantly different queries, they mostly result in similar responses that are rooted in the same harmful knowledge (e.g., detailed steps to make a bomb). Therefore, we conjecture that directly unlearn the harmful knowledge in the LLM can be a more effective way to defend against jailbreak attacks than the mainstream supervised fine-tuning (SFT) based approaches. Our extensive experiments confirmed our insight and suggested surprising generalizability of our unlearning-based approach: using only 20 raw harmful questions emph{without} any jailbreak prompt during training, our solution reduced the Attack Success Rate (ASR) in Vicuna-7B on emph{out-of-distribution} (OOD) harmful questions wrapped with various complex jailbreak prompts from 82.6% to 7.7%. This significantly outperforms Llama2-7B-Chat, which is fine-tuned on about 0.1M safety alignment samples but still has an ASR of 21.9% even under the help of an additional safety system prompt. Further analysis reveals that the generalization ability of our solution stems from the intrinsic relatedness among harmful responses across harmful questions (e.g., response patterns, shared steps and actions, and similarity among their learned representations in the LLM). Our code is available at url{https://github.com/thu-coai/SafeUnlearning}. | [
"['Zhexin Zhang' 'Junxiao Yang' 'Pei Ke' 'Shiyao Cui' 'Chujie Zheng'\n 'Hongning Wang' 'Minlie Huang']"
] |
null | null | 2407.02856 | null | null | http://arxiv.org/pdf/2407.02856v1 | 2024-07-03T07:14:25Z | 2024-07-03T07:14:25Z | Early-Stage Anomaly Detection: A Study of Model Performance on Complete
vs. Partial Flows | This study investigates the efficacy of machine learning models, specifically Random Forest, in anomaly detection systems when trained on complete flow records and tested on partial flow data. We explore the performance disparity that arises when models are applied to incomplete data typical in real-world, real-time network environments. Our findings demonstrate a significant decline in model performance, with precision and recall dropping by up to 30% under certain conditions when models trained on complete flows are tested against partial flows. Conversely, models trained and tested on consistently complete or partial datasets maintain robustness, highlighting the importance of dataset consistency in training. The study reveals that a minimum of 7 packets in the test set is required for maintaining reliable detection rates. These results underscore the need for tailored training strategies that can effectively adapt to the dynamics of partial data, enhancing the practical applicability of anomaly detection systems in operational settings. | [
"['Adrian Pekar' 'Richard Jozsa']"
] |
null | null | 2407.02861 | null | null | http://arxiv.org/pdf/2407.02861v1 | 2024-07-03T07:19:41Z | 2024-07-03T07:19:41Z | A Self-Supervised Task for Fault Detection in Satellite Multivariate
Time Series | In the space sector, due to environmental conditions and restricted accessibility, robust fault detection methods are imperative for ensuring mission success and safeguarding valuable assets. This work proposes a novel approach leveraging Physics-Informed Real NVP neural networks, renowned for their ability to model complex and high-dimensional distributions, augmented with a self-supervised task based on sensors' data permutation. It focuses on enhancing fault detection within the satellite multivariate time series. The experiments involve various configurations, including pre-training with self-supervision, multi-task learning, and standalone self-supervised training. Results indicate significant performance improvements across all settings. In particular, employing only the self-supervised loss yields the best overall results, suggesting its efficacy in guiding the network to extract relevant features for fault detection. This study presents a promising direction for improving fault detection in space systems and warrants further exploration in other datasets and applications. | [
"['Carlo Cena' 'Silvia Bucci' 'Alessandro Balossino' 'Marcello Chiaberge']"
] |
null | null | 2407.02870 | null | null | http://arxiv.org/pdf/2407.02870v1 | 2024-07-03T07:34:49Z | 2024-07-03T07:34:49Z | Membership Inference Attacks Against Time-Series Models | Analyzing time-series data that may contain personal information, particularly in the medical field, presents serious privacy concerns. Sensitive health data from patients is often used to train machine-learning models for diagnostics and ongoing care. Assessing the privacy risk of such models is crucial to making knowledgeable decisions on whether to use a model in production, share it with third parties, or deploy it in patients homes. Membership Inference Attacks (MIA) are a key method for this kind of evaluation, however time-series prediction models have not been thoroughly studied in this context. We explore existing MIA techniques on time-series models, and introduce new features, focusing on the seasonality and trend components of the data. Seasonality is estimated using a multivariate Fourier transform, and a low-degree polynomial is used to approximate trends. We applied these techniques to various types of time-series models, using datasets from the health domain. Our results demonstrate that these new features enhance the effectiveness of MIAs in identifying membership, improving the understanding of privacy risks in medical data applications. | [
"['Noam Koren' 'Abigail Goldsteen' 'Ariel Farkash' 'Guy Amit']"
] |
null | null | 2407.02880 | null | null | http://arxiv.org/pdf/2407.02880v1 | 2024-07-03T07:54:08Z | 2024-07-03T07:54:08Z | Knowledge Composition using Task Vectors with Learned Anisotropic
Scaling | Pre-trained models produce strong generic representations that can be adapted via fine-tuning. The learned weight difference relative to the pre-trained model, known as a task vector, characterises the direction and stride of fine-tuning. The significance of task vectors is such that simple arithmetic operations on them can be used to combine diverse representations from different domains. This paper builds on these properties of task vectors and aims to answer (1) whether components of task vectors, particularly parameter blocks, exhibit similar characteristics, and (2) how such blocks can be used to enhance knowledge composition and transfer. To this end, we introduce aTLAS, an algorithm that linearly combines parameter blocks with different learned coefficients, resulting in anisotropic scaling at the task vector level. We show that such linear combinations explicitly exploit the low intrinsic dimensionality of pre-trained models, with only a few coefficients being the learnable parameters. Furthermore, composition of parameter blocks leverages the already learned representations, thereby reducing the dependency on large amounts of data. We demonstrate the effectiveness of our method in task arithmetic, few-shot recognition and test-time adaptation, with supervised or unsupervised objectives. In particular, we show that (1) learned anisotropic scaling allows task vectors to be more disentangled, causing less interference in composition; (2) task vector composition excels with scarce or no labeled data and is less prone to domain shift, thus leading to better generalisability; (3) mixing the most informative parameter blocks across different task vectors prior to training can reduce the memory footprint and improve the flexibility of knowledge transfer. Moreover, we show the potential of aTLAS as a PEFT method, particularly with less data, and demonstrate that its scalibility. | [
"['Frederic Z. Zhang' 'Paul Albert' 'Cristian Rodriguez-Opazo'\n 'Anton van den Hengel' 'Ehsan Abbasnejad']"
] |
null | null | 2407.02881 | null | null | http://arxiv.org/pdf/2407.02881v1 | 2024-07-03T07:56:51Z | 2024-07-03T07:56:51Z | ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid
Computation | Operators devoid of multiplication, such as Shift and Add, have gained prominence for their compatibility with hardware. However, neural networks (NNs) employing these operators typically exhibit lower accuracy compared to conventional NNs with identical structures. ShiftAddAug uses costly multiplication to augment efficient but less powerful multiplication-free operators, improving performance without any inference overhead. It puts a ShiftAdd tiny NN into a large multiplicative model and encourages it to be trained as a sub-model to obtain additional supervision. In order to solve the weight discrepancy problem between hybrid operators, a new weight sharing method is proposed. Additionally, a novel two stage neural architecture search is used to obtain better augmentation effects for smaller but stronger multiplication-free tiny neural networks. The superiority of ShiftAddAug is validated through experiments in image classification and semantic segmentation, consistently delivering noteworthy enhancements. Remarkably, it secures up to a 4.95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs. | [
"['Yipin Guo' 'Zihao Li' 'Yilin Lang' 'Qinyuan Ren']"
] |
null | null | 2407.02888 | null | null | http://arxiv.org/pdf/2407.02888v1 | 2024-07-03T08:03:59Z | 2024-07-03T08:03:59Z | Joint Optimization of Resource Allocation and Data Selection for Fast
and Cost-Efficient Federated Edge Learning | Deploying federated learning at the wireless edge introduces federated edge learning (FEEL). Given FEEL's limited communication resources and potential mislabeled data on devices, improper resource allocation or data selection can hurt convergence speed and increase training costs. Thus, to realize an efficient FEEL system, this paper emphasizes jointly optimizing resource allocation and data selection. Specifically, in this work, through rigorously modeling the training process and deriving an upper bound on FEEL's one-round convergence rate, we establish a problem of joint resource allocation and data selection, which, unfortunately, cannot be solved directly. Toward this end, we equivalently transform the original problem into a solvable form via a variable substitution and then break it into two subproblems, that is, the resource allocation problem and the data selection problem. The two subproblems are mixed-integer non-convex and integer non-convex problems, respectively, and achieving their optimal solutions is a challenging task. Based on the matching theory and applying the convex-concave procedure and gradient projection methods, we devise a low-complexity suboptimal algorithm for the two subproblems, respectively. Finally, the superiority of our proposed scheme of joint resource allocation and data selection is validated by numerical results. | [
"['Yunjian Jia' 'Zhen Huang' 'Jiping Yan' 'Yulu Zhang' 'Kun Luo'\n 'Wanli Wen']"
] |
null | null | 2407.02891 | null | null | http://arxiv.org/pdf/2407.02891v1 | 2024-07-03T08:08:01Z | 2024-07-03T08:08:01Z | GPTQT: Quantize Large Language Models Twice to Push the Efficiency | Due to their large size, generative Large Language Models (LLMs) require significant computing and storage resources. This paper introduces a new post-training quantization method, GPTQT, to reduce memory usage and enhance processing speed by expressing the weight of LLM in 3bit/2bit. Practice has shown that minimizing the quantization error of weights is ineffective, leading to overfitting. Therefore, GPTQT employs a progressive two-step approach: initially quantizing weights using Linear quantization to a relatively high bit, followed by converting obtained int weight to lower bit binary coding. A re-explore strategy is proposed to optimize initial scaling factor. During inference, these steps are merged into pure binary coding, enabling efficient computation. Testing across various models and datasets confirms GPTQT's effectiveness. Compared to the strong 3-bit quantization baseline, GPTQT further reduces perplexity by 4.01 on opt-66B and increases speed by 1.24 times on opt-30b. The results on Llama2 show that GPTQT is currently the best binary coding quantization method for such kind of LLMs. | [
"['Yipin Guo' 'Yilin Lang' 'Qinyuan Ren']"
] |
null | null | 2407.02900 | null | null | http://arxiv.org/pdf/2407.02900v1 | 2024-07-03T08:20:27Z | 2024-07-03T08:20:27Z | Self-supervised Vision Transformer are Scalable Generative Models for
Domain Generalization | Despite notable advancements, the integration of deep learning (DL) techniques into impactful clinical applications, particularly in the realm of digital histopathology, has been hindered by challenges associated with achieving robust generalization across diverse imaging domains and characteristics. Traditional mitigation strategies in this field such as data augmentation and stain color normalization have proven insufficient in addressing this limitation, necessitating the exploration of alternative methodologies. To this end, we propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches and seamlessly infuse them into the original images, thereby creating novel, synthetic images with diverse attributes. By enriching the dataset with such synthesized images, we aim to enhance its holistic nature, facilitating improved generalization of DL models to unseen domains. Extensive experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach, outperforming the state of the art substantially, on the Camelyon17-wilds challenge dataset (+2%) and on a second epithelium-stroma dataset (+26%). Furthermore, we emphasize our method's ability to readily scale with increasingly available unlabeled data samples and more complex, higher parametric architectures. Source code is available at https://github.com/sdoerrich97/vits-are-generative-models . | [
"['Sebastian Doerrich' 'Francesco Di Salvo' 'Christian Ledig']"
] |
null | null | 2407.02904 | null | null | http://arxiv.org/pdf/2407.02904v1 | 2024-07-03T08:23:02Z | 2024-07-03T08:23:02Z | The Shortcomings of Force-from-Motion in Robot Learning | Robotic manipulation requires accurate motion and physical interaction control. However, current robot learning approaches focus on motion-centric action spaces that do not explicitly give the policy control over the interaction. In this paper, we discuss the repercussions of this choice and argue for more interaction-explicit action spaces in robot learning. | [
"['Elie Aljalbout' 'Felix Frank' 'Patrick van der Smagt'\n 'Alexandros Paraschos']"
] |
null | null | 2407.02913 | null | null | http://arxiv.org/pdf/2407.02913v1 | 2024-07-03T08:38:14Z | 2024-07-03T08:38:14Z | SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic | Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models. However, these algorithms depend on high-precision arithmetic to maintain inference accuracy, which conflicts with the model quantization. To resolve this conflict and further improve the efficiency of quantized convolution, we proposes SFC, a new algebra transform for fast convolution by extending the Discrete Fourier Transform (DFT) with symbolic computing, in which only additions are required to perform the transformation at specific transform points, avoiding the calculation of irrational number and reducing the requirement for precision. Additionally, we enhance convolution efficiency by introducing correction terms to convert invalid circular convolution outputs of the Fourier method into effective ones. The numerical error analysis is presented for the first time in this type of work and proves that our algorithms can provide a 3.68x multiplication reduction for 3x3 convolution, while the Winograd algorithm only achieves a 2.25x reduction with similarly low numerical errors. Experiments carried out on benchmarks and FPGA show that our new algorithms can further improve the computation efficiency of quantized models while maintaining accuracy, surpassing both the quantization-alone method and existing works on fast convolution quantization. | [
"['Liulu He' 'Yufei Zhao' 'Rui Gao' 'Yuan Du' 'Li Du']"
] |
null | null | 2407.02914 | null | null | http://arxiv.org/pdf/2407.02914v1 | 2024-07-03T08:45:17Z | 2024-07-03T08:45:17Z | The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design
Trade-Offs in Ensemble Learning Systems | Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and fusing their predictions, has been studied extensively for accuracy, we have insufficient knowledge about how to design energy-efficient ensembles. Objective: We therefore analyzed three types of design decisions for ensemble learning regarding a potential trade-off between accuracy and energy consumption: a) ensemble size, i.e., the number of models in the ensemble, b) fusion methods (majority voting vs. a meta-model), and c) partitioning methods (whole-dataset vs. subset-based training). Methods: By combining four popular ML algorithms for classification in different ensembles, we conducted a full factorial experiment with 11 ensembles x 4 datasets x 2 fusion methods x 2 partitioning methods (176 combinations). For each combination, we measured accuracy (F1-score) and energy consumption in J (for both training and inference). Results: While a larger ensemble size significantly increased energy consumption (size 2 ensembles consumed 37.49% less energy than size 3 ensembles, which in turn consumed 26.96% less energy than the size 4 ensembles), it did not significantly increase accuracy. Furthermore, majority voting outperformed meta-model fusion both in terms of accuracy (Cohen's d of 0.38) and energy consumption (Cohen's d of 0.92). Lastly, subset-based training led to significantly lower energy consumption (Cohen's d of 0.91), while training on the whole dataset did not increase accuracy significantly. Conclusions: From a Green AI perspective, we recommend designing ensembles of small size (2 or maximum 3 models), using subset-based training, majority voting, and energy-efficient ML algorithms like decision trees, Naive Bayes, or KNN. | [
"['Rafiullah Omar' 'Justus Bogner' 'Henry Muccini' 'Patricia Lago'\n 'Silverio Martínez-Fernández' 'Xavier Franch']"
] |
null | null | 2407.02943 | null | null | http://arxiv.org/pdf/2407.02943v1 | 2024-07-03T09:20:04Z | 2024-07-03T09:20:04Z | PII-Compass: Guiding LLM training data extraction prompts towards the
target PII via grounding | The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output personal identifiable information (PII) contained in their training data. However, reported PIII extraction performance varies widely, and there is no consensus on the optimal methodology to evaluate this risk, resulting in underestimating realistic adversaries. In this work, we empirically demonstrate that it is possible to improve the extractability of PII by over ten-fold by grounding the prefix of the manually constructed extraction prompt with in-domain data. Our approach, PII-Compass, achieves phone number extraction rates of 0.92%, 3.9%, and 6.86% with 1, 128, and 2308 queries, respectively, i.e., the phone number of 1 person in 15 is extractable. | [
"['Krishna Kanth Nakka' 'Ahmed Frikha' 'Ricardo Mendes' 'Xue Jiang'\n 'Xuebing Zhou']"
] |
null | null | 2407.02956 | null | null | http://arxiv.org/pdf/2407.02956v1 | 2024-07-03T09:49:03Z | 2024-07-03T09:49:03Z | IncogniText: Privacy-enhancing Conditional Text Anonymization via
LLM-based Private Attribute Randomization | In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics. We propose IncogniText, a technique that anonymizes the text to mislead a potential adversary into predicting a wrong private attribute value. Our empirical evaluation shows a reduction of private attribute leakage by more than 90%. Finally, we demonstrate the maturity of IncogniText for real-world applications by distilling its anonymization capability into a set of LoRA parameters associated with an on-device model. | [
"['Ahmed Frikha' 'Nassim Walha' 'Krishna Kanth Nakka' 'Ricardo Mendes'\n 'Xue Jiang' 'Xuebing Zhou']"
] |
null | null | 2407.02960 | null | null | http://arxiv.org/pdf/2407.02960v1 | 2024-07-03T09:54:08Z | 2024-07-03T09:54:08Z | ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary
LLMs on Private Datasets | This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private data of another data owner entity, in a way that ensures the confidentiality of both the model and the data. Hereby, the finetuning is conducted offsite, i.e., on the computation infrastructure of a third-party cloud provider. We tackle this problem by proposing ObfuscaTune, a novel, efficient and fully utility-preserving approach that combines a simple yet effective obfuscation technique with an efficient usage of confidential computing (only 5% of the model parameters are placed on TEE). We empirically demonstrate the effectiveness of ObfuscaTune by validating it on GPT-2 models with different sizes on four NLP benchmark datasets. Finally, we compare to a na"ive version of our approach to highlight the necessity of using random matrices with low condition numbers in our approach to reduce errors induced by the obfuscation. | [
"['Ahmed Frikha' 'Nassim Walha' 'Ricardo Mendes' 'Krishna Kanth Nakka'\n 'Xue Jiang' 'Xuebing Zhou']"
] |
null | null | 2407.02961 | null | null | http://arxiv.org/pdf/2407.02961v1 | 2024-07-03T09:54:58Z | 2024-07-03T09:54:58Z | Towards a Scalable Reference-Free Evaluation of Generative Models | While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference datasets. Recently, the reference-free entropy scores, VENDI and RKE, have been proposed to evaluate the diversity of generated data. However, estimating these scores from data leads to significant computational costs for large-scale generative models. In this work, we leverage the random Fourier features framework to reduce the computational price and propose the Fourier-based Kernel Entropy Approximation (FKEA) method. We utilize FKEA's approximated eigenspectrum of the kernel matrix to efficiently estimate the mentioned entropy scores. Furthermore, we show the application of FKEA's proxy eigenvectors to reveal the method's identified modes in evaluating the diversity of produced samples. We provide a stochastic implementation of the FKEA assessment algorithm with a complexity $O(n)$ linearly growing with sample size $n$. We extensively evaluate FKEA's numerical performance in application to standard image, text, and video datasets. Our empirical results indicate the method's scalability and interpretability applied to large-scale generative models. The codebase is available at https://github.com/aziksh-ospanov/FKEA. | [
"['Azim Ospanov' 'Jingwei Zhang' 'Mohammad Jalali' 'Xuenan Cao'\n 'Andrej Bogdanov' 'Farzan Farnia']"
] |
null | null | 2407.02974 | null | null | http://arxiv.org/pdf/2407.02974v1 | 2024-07-03T10:14:33Z | 2024-07-03T10:14:33Z | IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided
Implicit Neural Representations | Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long acquisition times and can compromise the clinical utility of acquired images. Traditional motion correction methods often fail to address severe motion, leading to distorted and unreliable results. Deep Learning (DL) alleviated such pitfalls through generalization with the cost of vanishing structures and hallucinations, making it challenging to apply in the medical field where hallucinated structures can tremendously impact the diagnostic outcome. In this work, we present an instance-wise motion correction pipeline that leverages motion-guided Implicit Neural Representations (INRs) to mitigate the impact of motion artifacts while retaining anatomical structure. Our method is evaluated using the NYU fastMRI dataset with different degrees of simulated motion severity. For the correction alone, we can improve over state-of-the-art image reconstruction methods by $+5%$ SSIM, $+5:db$ PSNR, and $+14%$ HaarPSI. Clinical relevance is demonstrated by a subsequent experiment, where our method improves classification outcomes by at least $+1.5$ accuracy percentage points compared to motion-corrupted images. | [
"['Ziad Al-Haj Hemidi' 'Christian Weihsbach' 'Mattias P. Heinrich']"
] |
null | null | 2407.02984 | null | null | http://arxiv.org/pdf/2407.02984v2 | 2024-07-05T10:48:27Z | 2024-07-03T10:31:30Z | Semantically Rich Local Dataset Generation for Explainable AI in
Genomics | Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. Therefore, interpreting these models may provide novel insights into the underlying biology, supporting downstream biomedical applications. Due to their complexity, interpretable surrogate models can only be built for local explanations (e.g., a single instance). However, accomplishing this requires generating a dataset in the neighborhood of the input, which must maintain syntactic similarity to the original data while introducing semantic variability in the model's predictions. This task is challenging due to the complex sequence-to-function relationship of DNA. We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity. Our custom, domain-guided individual representation effectively constrains syntactic similarity, and we provide two alternative fitness functions that promote diversity with no computational effort. Applied to the RNA splicing domain, our approach quickly achieves good diversity and significantly outperforms a random baseline in exploring the search space, as shown by our proof-of-concept, short RNA sequence. Furthermore, we assess its generalizability and demonstrate scalability to larger sequences, resulting in a ~30% improvement over the baseline. | [
"['Pedro Barbosa' 'Rosina Savisaar' 'Alcides Fonseca']"
] |
null | null | 2407.02987 | null | null | http://arxiv.org/pdf/2407.02987v1 | 2024-07-03T10:38:40Z | 2024-07-03T10:38:40Z | LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content
Moderation of Large Language Models | Guardrails have emerged as an alternative to safety alignment for content moderation of large language models (LLMs). Existing model-based guardrails have not been designed for resource-constrained computational portable devices, such as mobile phones, more and more of which are running LLM-based applications locally. We introduce LoRA-Guard, a parameter-efficient guardrail adaptation method that relies on knowledge sharing between LLMs and guardrail models. LoRA-Guard extracts language features from the LLMs and adapts them for the content moderation task using low-rank adapters, while a dual-path design prevents any performance degradation on the generative task. We show that LoRA-Guard outperforms existing approaches with 100-1000x lower parameter overhead while maintaining accuracy, enabling on-device content moderation. | [
"['Hayder Elesedy' 'Pedro M. Esperança' 'Silviu Vlad Oprea' 'Mete Ozay']"
] |
null | null | 2407.03038 | null | null | http://arxiv.org/pdf/2407.03038v1 | 2024-07-03T12:02:24Z | 2024-07-03T12:02:24Z | On the Client Preference of LLM Fine-tuning in Federated Learning | Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using preference datasets, enabling the LLM to generate outputs that align with human preferences. Given the sensitive nature of these preference datasets held by various clients, there is a need to implement RLHF within a federated learning (FL) framework, where clients are reluctant to share their data due to privacy concerns. To address this, we introduce a feasible framework in which clients collaboratively train a binary selector with their preference datasets using our proposed FedBis. With a well-trained selector, we can further enhance the LLM that generates human-preferred completions. Meanwhile, we propose a novel algorithm, FedBiscuit, that trains multiple selectors by organizing clients into balanced and disjoint clusters based on their preferences. Compared to the FedBis, FedBiscuit demonstrates superior performance in simulating human preferences for pairwise completions. Our extensive experiments on federated human preference datasets -- marking the first benchmark to address heterogeneous data partitioning among clients -- demonstrate that FedBiscuit outperforms FedBis and even surpasses traditional centralized training. | [
"['Feijie Wu' 'Xiaoze Liu' 'Haoyu Wang' 'Xingchen Wang' 'Jing Gao']"
] |
null | null | 2407.03045 | null | null | http://arxiv.org/pdf/2407.03045v1 | 2024-07-03T12:10:41Z | 2024-07-03T12:10:41Z | JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts
Discovery from Large-Scale Human-LLM Conversational Datasets | Large Language Models (LLMs) have gained significant attention but also raised concerns due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards LLMs, have appeared and constantly evolved to breach the safety protocols of LLMs. To address this issue, LLMs are regularly updated with safety patches based on reported jailbreak prompts. However, malicious users often keep their successful jailbreak prompts private to exploit LLMs. To uncover these private jailbreak prompts, extensive analysis of large-scale conversational datasets is necessary to identify prompts that still manage to bypass the system's defenses. This task is highly challenging due to the immense volume of conversation data, diverse characteristics of jailbreak prompts, and their presence in complex multi-turn conversations. To tackle these challenges, we introduce JailbreakHunter, a visual analytics approach for identifying jailbreak prompts in large-scale human-LLM conversational datasets. We have designed a workflow with three analysis levels: group-level, conversation-level, and turn-level. Group-level analysis enables users to grasp the distribution of conversations and identify suspicious conversations using multiple criteria, such as similarity with reported jailbreak prompts in previous research and attack success rates. Conversation-level analysis facilitates the understanding of the progress of conversations and helps discover jailbreak prompts within their conversation contexts. Turn-level analysis allows users to explore the semantic similarity and token overlap between a singleturn prompt and the reported jailbreak prompts, aiding in the identification of new jailbreak strategies. The effectiveness and usability of the system were verified through multiple case studies and expert interviews. | [
"['Zhihua Jin' 'Shiyi Liu' 'Haotian Li' 'Xun Zhao' 'Huamin Qu']"
] |
null | null | 2407.03056 | null | null | http://arxiv.org/pdf/2407.03056v1 | 2024-07-03T12:24:40Z | 2024-07-03T12:24:40Z | Improving Zero-shot Generalization of Learned Prompts via Unsupervised
Knowledge Distillation | Vision-Language Models (VLMs) demonstrate remarkable zero-shot generalization to unseen tasks, but fall short of the performance of supervised methods in generalizing to downstream tasks with limited data. Prompt learning is emerging as a parameter-efficient method for adapting VLMs, but state-of-the-art approaches require annotated samples. In this paper we propose a novel approach to prompt learning based on unsupervised knowledge distillation from more powerful models. Our approach, which we call Knowledge Distillation Prompt Learning (KDPL), can be integrated into existing prompt learning techniques and eliminates the need for labeled examples during adaptation. Our experiments on more than ten standard benchmark datasets demonstrate that KDPL is very effective at improving generalization of learned prompts for zero-shot domain generalization, zero-shot cross-dataset generalization, and zero-shot base-to-novel class generalization problems. KDPL requires no ground-truth labels for adaptation, and moreover we show that even in the absence of any knowledge of training class names it can be used to effectively transfer knowledge. The code is publicly available at https://github.com/miccunifi/KDPL. | [
"['Marco Mistretta' 'Alberto Baldrati' 'Marco Bertini' 'Andrew D. Bagdanov']"
] |
null | null | 2407.03059 | null | null | http://arxiv.org/pdf/2407.03059v1 | 2024-07-03T12:30:39Z | 2024-07-03T12:30:39Z | FairJob: A Real-World Dataset for Fairness in Online Systems | We introduce a fairness-aware dataset for job recommendation in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business confidentiality. An additional challenge is the lack of access to protected user attributes such as gender, for which we propose a solution to obtain a proxy estimate. Despite being anonymized and including a proxy for a sensitive attribute, our dataset preserves predictive power and maintains a realistic and challenging benchmark. This dataset addresses a significant gap in the availability of fairness-focused resources for high-impact domains like advertising -- the actual impact being having access or not to precious employment opportunities, where balancing fairness and utility is a common industrial challenge. We also explore various stages in the advertising process where unfairness can occur and introduce a method to compute a fair utility metric for the job recommendations in online systems case from a biased dataset. Experimental evaluations of bias mitigation techniques on the released dataset demonstrate potential improvements in fairness and the associated trade-offs with utility. | [
"['Mariia Vladimirova' 'Federico Pavone' 'Eustache Diemert']"
] |
null | null | 2407.03065 | null | null | http://arxiv.org/pdf/2407.03065v1 | 2024-07-03T12:36:24Z | 2024-07-03T12:36:24Z | Warm-up Free Policy Optimization: Improved Regret in Linear Markov
Decision Processes | Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with rate-optimal regret guarantees under the linear Markov Decision Process (MDP) model. However, their algorithm relies on a costly pure exploration warm-up phase that is hard to implement in practice. This paper eliminates this undesired warm-up phase, replacing it with a simple and efficient contraction mechanism. Our PO algorithm achieves rate-optimal regret with improved dependence on the other parameters of the problem (horizon and function approximation dimension) in two fundamental settings: adversarial losses with full-information feedback and stochastic losses with bandit feedback. | [
"['Asaf Cassel' 'Aviv Rosenberg']"
] |
null | null | 2407.03080 | null | null | http://arxiv.org/pdf/2407.03080v1 | 2024-07-03T12:53:42Z | 2024-07-03T12:53:42Z | Artificial Inductive Bias for Synthetic Tabular Data Generation in
Data-Scarce Scenarios | While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on substantial training data, often unavailable in real-world applications. This paper addresses this challenge by proposing a novel methodology for generating realistic and reliable synthetic tabular data with DGMs in limited real-data environments. Our approach proposes several ways to generate an artificial inductive bias in a DGM through transfer learning and meta-learning techniques. We explore and compare four different methods within this framework, demonstrating that transfer learning strategies like pre-training and model averaging outperform meta-learning approaches, like Model-Agnostic Meta-Learning, and Domain Randomized Search. We validate our approach using two state-of-the-art DGMs, namely, a Variational Autoencoder and a Generative Adversarial Network, to show that our artificial inductive bias fuels superior synthetic data quality, as measured by Jensen-Shannon divergence, achieving relative gains of up to 50% when using our proposed approach. This methodology has broad applicability in various DGMs and machine learning tasks, particularly in areas like healthcare and finance, where data scarcity is often a critical issue. | [
"['Patricia A. Apellániz' 'Ana Jiménez' 'Borja Arroyo Galende'\n 'Juan Parras' 'Santiago Zazo']"
] |
null | null | 2407.03082 | null | null | http://arxiv.org/pdf/2407.03082v1 | 2024-07-03T13:03:51Z | 2024-07-03T13:03:51Z | Stable Heterogeneous Treatment Effect Estimation across
Out-of-Distribution Populations | Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation Learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. | [
"['Yuling Zhang' 'Anpeng Wu' 'Kun Kuang' 'Liang Du' 'Zixun Sun' 'Zhi Wang']"
] |
null | null | 2407.03086 | null | null | http://arxiv.org/pdf/2407.03086v1 | 2024-07-03T13:15:12Z | 2024-07-03T13:15:12Z | Effective Heterogeneous Federated Learning via Efficient
Hypernetwork-based Weight Generation | While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients' resources and careful parameter aggregation to accommodate this heterogeneity. We propose HypeMeFed, a novel federated learning framework for supporting client heterogeneity by combining a multi-exit network architecture with hypernetwork-based model weight generation. This approach aligns the feature spaces of heterogeneous model layers and resolves per-layer information disparity during weight aggregation. To practically realize HypeMeFed, we also propose a low-rank factorization approach to minimize computation and memory overhead associated with hypernetworks. Our evaluations on a real-world heterogeneous device testbed indicate that HypeMeFed enhances accuracy by 5.12% over FedAvg, reduces the hypernetwork memory requirements by 98.22%, and accelerates its operations by 1.86 times compared to a naive hypernetwork approach. These results demonstrate HypeMeFed's effectiveness in leveraging and engaging heterogeneous clients for federated learning. | [
"['Yujin Shin' 'Kichang Lee' 'Sungmin Lee' 'You Rim Choi' 'Hyung-Sin Kim'\n 'JeongGil Ko']"
] |
null | null | 2407.03089 | null | null | http://arxiv.org/pdf/2407.03089v2 | 2024-07-04T04:11:57Z | 2024-07-03T13:26:31Z | Spatio-Temporal Adaptive Diffusion Models for EEG Super-Resolution in
Epilepsy Diagnosis | Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, meeting the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STADMs) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which then serve as conditional inputs to guide the reverse denoising process of diffusion models. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the proposed method effectively enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STADMs demonstrate their value by applying synthetic SR EEG to classification and source localization tasks of epilepsy patients, indicating their potential to significantly improve the spatial resolution of LR EEG. | [
"['Tong Zhou' 'Shuqiang Wang']"
] |
null | null | 2407.03093 | null | null | http://arxiv.org/pdf/2407.03093v1 | 2024-07-03T13:34:30Z | 2024-07-03T13:34:30Z | Revisiting the Performance of Deep Learning-Based Vulnerability
Detection on Realistic Datasets | The impact of software vulnerabilities on everyday software systems is significant. Despite deep learning models being proposed for vulnerability detection, their reliability is questionable. Prior evaluations show high recall/F1 scores of up to 99%, but these models underperform in practical scenarios, particularly when assessed on entire codebases rather than just the fixing commit. This paper introduces Real-Vul, a comprehensive dataset representing real-world scenarios for evaluating vulnerability detection models. Evaluating DeepWukong, LineVul, ReVeal, and IVDetect shows a significant drop in performance, with precision decreasing by up to 95 percentage points and F1 scores by up to 91 points. Furthermore, Model performance fluctuates based on vulnerability characteristics, with better F1 scores for information leaks or code injection than for path resolution or predictable return values. The results highlight a significant performance gap that needs addressing before deploying deep learning-based vulnerability detection in practical settings. Overfitting is identified as a key issue, and an augmentation technique is proposed, potentially improving performance by up to 30%. Contributions include a dataset creation approach for better model evaluation, Real-Vul dataset, and empirical evidence of deep learning models struggling in real-world settings. | [
"['Partha Chakraborty' 'Krishna Kanth Arumugam' 'Mahmoud Alfadel'\n 'Meiyappan Nagappan' 'Shane McIntosh']"
] |
null | null | 2407.03094 | null | null | http://arxiv.org/pdf/2407.03094v1 | 2024-07-03T13:34:33Z | 2024-07-03T13:34:33Z | Conformal Prediction for Causal Effects of Continuous Treatments | Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample prediction intervals for potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data. | [
"['Maresa Schröder' 'Dennis Frauen' 'Jonas Schweisthal' 'Konstantin Heß'\n 'Valentyn Melnychuk' 'Stefan Feuerriegel']"
] |
null | null | 2407.03105 | null | null | http://arxiv.org/pdf/2407.03105v1 | 2024-07-03T13:42:21Z | 2024-07-03T13:42:21Z | On Generalization for Generative Flow Networks | Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an unnormalized probability distribution, called the reward function. This framework learns a policy on a constructed graph, which enables sampling from an approximation of the target probability distribution through successive steps of sampling from the learned policy. To achieve this, GFlowNets can be trained with various objectives, each of which can lead to the model s ultimate goal. The aspirational strength of GFlowNets lies in their potential to discern intricate patterns within the reward function and their capacity to generalize effectively to novel, unseen parts of the reward function. This paper attempts to formalize generalization in the context of GFlowNets, to link generalization with stability, and also to design experiments that assess the capacity of these models to uncover unseen parts of the reward function. The experiments will focus on length generalization meaning generalization to states that can be constructed only by longer trajectories than those seen in training. | [
"['Anas Krichel' 'Nikolay Malkin' 'Salem Lahlou' 'Yoshua Bengio']"
] |
null | null | 2407.03108 | null | null | http://arxiv.org/pdf/2407.03108v1 | 2024-07-03T13:47:41Z | 2024-07-03T13:47:41Z | How Reliable and Stable are Explanations of XAI Methods? | Black box models are increasingly being used in the daily lives of human beings living in society. Along with this increase, there has been the emergence of Explainable Artificial Intelligence (XAI) methods aimed at generating additional explanations regarding how the model makes certain predictions. In this sense, methods such as Dalex, Eli5, eXirt, Lofo and Shap emerged as different proposals and methodologies for generating explanations of black box models in an agnostic way. Along with the emergence of these methods, questions arise such as "How Reliable and Stable are XAI Methods?". With the aim of shedding light on this main question, this research creates a pipeline that performs experiments using the diabetes dataset and four different machine learning models (LGBM, MLP, DT and KNN), creating different levels of perturbations of the test data and finally generates explanations from the eXirt method regarding the confidence of the models and also feature relevances ranks from all XAI methods mentioned, in order to measure their stability in the face of perturbations. As a result, it was found that eXirt was able to identify the most reliable models among all those used. It was also found that current XAI methods are sensitive to perturbations, with the exception of one specific method. | [
"['José Ribeiro' 'Lucas Cardoso' 'Vitor Santos' 'Eduardo Carvalho'\n 'Níkolas Carneiro' 'Ronnie Alves']"
] |
null | null | 2407.03111 | null | null | http://arxiv.org/pdf/2407.03111v2 | 2024-07-04T08:07:18Z | 2024-05-08T09:03:17Z | Compressed Latent Replays for Lightweight Continual Learning on Spiking
Neural Networks | Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been explored in depth. In this paper we introduce the first memory-efficient implementation of Latent Replay (LR)-based CL for SNNs, designed to seamlessly integrate with resource-constrained devices. LRs combine new samples with latent representations of previously learned data, to mitigate forgetting. Experiments on the Heidelberg SHD dataset with Sample and Class-Incremental tasks reach a Top-1 accuracy of 92.5% and 92%, respectively, without forgetting the previously learned information. Furthermore, we minimize the LRs' requirements by applying a time-domain compression, reducing by two orders of magnitude their memory requirement, with respect to a naive rehearsal setup, with a maximum accuracy drop of 4%. On a Multi-Class-Incremental task, our SNN learns 10 new classes from an initial set of 10, reaching a Top-1 accuracy of 78.4% on the full test set. | [
"['Alberto Dequino' 'Alessio Carpegna' 'Davide Nadalini'\n 'Alessandro Savino' 'Luca Benini' 'Stefano Di Carlo' 'Francesco Conti']"
] |
null | null | 2407.03118 | null | null | http://arxiv.org/pdf/2407.03118v2 | 2024-07-08T08:07:35Z | 2024-07-03T14:04:05Z | Can machine learning solve the challenge of adaptive learning and the
individualization of learning paths? A field experiment in an online learning
platform | The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualization can be implemented. To tackle this question we conduct a randomized controlled trial on a large digital self-learning platform. We develop an algorithm based on two convolutional neural networks that assigns tasks to $4,365$ learners according to their learning paths. Learners are randomized into three groups: two treatment groups -- a group-based adaptive treatment group and an individual adaptive treatment group -- and one control group. We analyze the difference between the three groups with respect to effort learners provide and their performance on the platform. Our null results shed light on the multiple challenges associated with the individualization of learning paths. | [
"['Marius Köppel' 'Tim Klausmann' 'Isabell Zipperle' 'Daniel Schunk']"
] |
null | null | 2407.03125 | null | null | http://arxiv.org/pdf/2407.03125v2 | 2024-07-08T01:22:37Z | 2024-07-03T14:07:41Z | Foundations and Frontiers of Graph Learning Theory | Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. With these models being usually characterized by intuition-driven design or highly intricate components, placing them within the theoretical analysis framework to distill the core concepts, helps understand the key principles that drive the functionality better and guide further development. Given this surge in interest, this article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models. Encompassing discussions on fundamental aspects such as expressiveness power, generalization, optimization, and unique phenomena such as over-smoothing and over-squashing, this piece delves into the theoretical foundations and frontier driving the evolution of graph learning. In addition, this article also presents several challenges and further initiates discussions on possible solutions. | [
"['Yu Huang' 'Min Zhou' 'Menglin Yang' 'Zhen Wang' 'Muhan Zhang' 'Jie Wang'\n 'Hong Xie' 'Hao Wang' 'Defu Lian' 'Enhong Chen']"
] |
null | null | 2407.03132 | null | null | http://arxiv.org/pdf/2407.03132v1 | 2024-07-03T14:13:04Z | 2024-07-03T14:13:04Z | Speaker- and Text-Independent Estimation of Articulatory Movements and
Phoneme Alignments from Speech | This paper introduces a novel combination of two tasks, previously treated separately: acoustic-to-articulatory speech inversion (AAI) and phoneme-to-articulatory (PTA) motion estimation. We refer to this joint task as acoustic phoneme-to-articulatory speech inversion (APTAI) and explore two different approaches, both working speaker- and text-independently during inference. We use a multi-task learning setup, with the end-to-end goal of taking raw speech as input and estimating the corresponding articulatory movements, phoneme sequence, and phoneme alignment. While both proposed approaches share these same requirements, they differ in their way of achieving phoneme-related predictions: one is based on frame classification, the other on a two-staged training procedure and forced alignment. We reach competitive performance of 0.73 mean correlation for the AAI task and achieve up to approximately 87% frame overlap compared to a state-of-the-art text-dependent phoneme force aligner. | [
"['Tobias Weise' 'Philipp Klumpp' 'Kubilay Can Demir'\n 'Paula Andrea Pérez-Toro' 'Maria Schuster' 'Elmar Noeth'\n 'Bjoern Heismann' 'Andreas Maier' 'Seung Hee Yang']"
] |
null | null | 2407.03133 | null | null | http://arxiv.org/pdf/2407.03133v2 | 2024-07-11T09:19:11Z | 2024-05-24T08:10:31Z | Quantifying the Cross-sectoral Intersecting Discrepancies within
Multiple Groups Using Latent Class Analysis Towards Fairness | The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including EVENS and Census 2021 (England & Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications. Additionally, we demonstrate how the proposed approach can be used to provide insights into the fairness of machine learning. | [
"['Yingfang Yuan' 'Kefan Chen' 'Mehdi Rizvi' 'Lynne Baillie' 'Wei Pang']"
] |
null | null | 2407.03146 | null | null | http://arxiv.org/pdf/2407.03146v2 | 2024-07-08T05:21:59Z | 2024-05-31T02:56:43Z | Enhancing Class Fairness in Classification with A Two-Player Game
Approach | Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed in some downstream tasks, data augmentation may introduce an unfair impact on classifications. While it can improve the performance of some classes, it can actually be detrimental for other classes, which can be problematic in some application domains. In this paper, to counteract this phenomenon, we propose a FAir Classification approach with a Two-player game (FACT). We first formulate the training of a classifier with data augmentation as a fair optimization problem, which can be further written as an adversarial two-player game. Following this formulation, we propose a novel multiplicative weight optimization algorithm, for which we theoretically prove that it can converge to a solution that is fair over classes. Interestingly, our formulation also reveals that this fairness issue over classes is not due to data augmentation only, but is in fact a general phenomenon. Our empirical experiments demonstrate that the performance of our learned classifiers is indeed more fairly distributed over classes in five datasets, with only limited impact on the average accuracy. | [
"['Yunpeng Jiang' 'Paul Weng' 'Yutong Ban']"
] |
null | null | 2407.03152 | null | null | http://arxiv.org/pdf/2407.03152v1 | 2024-07-03T14:30:47Z | 2024-07-03T14:30:47Z | Stereo Risk: A Continuous Modeling Approach to Stereo Matching | We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. As it is well-known that stereo matching boils down to a per-pixel disparity estimation problem, the popular state-of-the-art stereo-matching approaches widely rely on regressing the scene disparity values, yet via discretization of scene disparity values. Such discretization often fails to capture the nuanced, continuous nature of scene depth. Stereo Risk departs from the conventional discretization approach by formulating the scene disparity as an optimal solution to a continuous risk minimization problem, hence the name "stereo risk". We demonstrate that $L^1$ minimization of the proposed continuous risk function enhances stereo-matching performance for deep networks, particularly for disparities with multi-modal probability distributions. Furthermore, to enable the end-to-end network training of the non-differentiable $L^1$ risk optimization, we exploited the implicit function theorem, ensuring a fully differentiable network. A comprehensive analysis demonstrates our method's theoretical soundness and superior performance over the state-of-the-art methods across various benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, SceneFlow, and Middlebury 2014. | [
"['Ce Liu' 'Suryansh Kumar' 'Shuhang Gu' 'Radu Timofte' 'Yao Yao'\n 'Luc Van Gool']"
] |
null | null | 2407.03153 | null | null | http://arxiv.org/pdf/2407.03153v1 | 2024-06-09T17:42:09Z | 2024-06-09T17:42:09Z | Efficient Shapley Values for Attributing Global Properties of Diffusion
Models to Data Group | As diffusion models are deployed in real-world settings, data attribution is needed to ensure fair acknowledgment for contributors of high-quality training data and to identify sources of harmful content. Previous work focuses on identifying individual training samples important for the generation of a given image. However, instead of focusing on a given generated image, some use cases require understanding global properties of the distribution learned by a diffusion model (e.g., demographic diversity). Furthermore, training data for diffusion models are often contributed in groups rather than separately (e.g., multiple artworks from the same artist). Hence, here we tackle the problem of attributing global properties of diffusion models to groups of training data. Specifically, we develop a method to efficiently estimate Shapley values by leveraging model pruning and fine-tuning. We empirically demonstrate the utility of our method with three use cases: (i) global image quality for a DDPM trained on a CIFAR dataset, (ii) demographic diversity for an LDM trained on CelebA-HQ, and (iii) overall aesthetic quality for a Stable Diffusion model LoRA-finetuned on Post-Impressionist artworks. | [
"['Chris Lin' 'Mingyu Lu' 'Chanwoo Kim' 'Su-In Lee']"
] |
null | null | 2407.03154 | null | null | http://arxiv.org/pdf/2407.03154v1 | 2024-07-03T14:31:36Z | 2024-07-03T14:31:36Z | Reinforcement Learning for Sequence Design Leveraging Protein Language
Models | Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery. Prior approaches have resorted to evolutionary strategies or Monte-Carlo methods for protein design, but often fail to exploit the structure of the combinatorial search space, to generalize to unseen sequences. In the context of discrete black box optimization over large search spaces, learning a mutation policy to generate novel sequences with reinforcement learning is appealing. Recent advances in protein language models (PLMs) trained on large corpora of protein sequences offer a potential solution to this problem by scoring proteins according to their biological plausibility (such as the TM-score). In this work, we propose to use PLMs as a reward function to generate new sequences. Yet the PLM can be computationally expensive to query due to its large size. To this end, we propose an alternative paradigm where optimization can be performed on scores from a smaller proxy model that is periodically finetuned, jointly while learning the mutation policy. We perform extensive experiments on various sequence lengths to benchmark RL-based approaches, and provide comprehensive evaluations along biological plausibility and diversity of the protein. Our experimental results include favorable evaluations of the proposed sequences, along with high diversity scores, demonstrating that RL is a strong candidate for biological sequence design. Finally, we provide a modular open source implementation can be easily integrated in most RL training loops, with support for replacing the reward model with other PLMs, to spur further research in this domain. The code for all experiments is provided in the supplementary material. | [
"['Jithendaraa Subramanian' 'Shivakanth Sujit' 'Niloy Irtisam' 'Umong Sain'\n 'Derek Nowrouzezahrai' 'Samira Ebrahimi Kahou' 'Riashat Islam']"
] |
Subsets and Splits