categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.05765
| null | null |
http://arxiv.org/pdf/2403.05765v1
|
2024-03-09T02:24:02Z
|
2024-03-09T02:24:02Z
|
Physics-informed Neural Motion Planning on Constraint Manifolds
|
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds. These problems appear in various scenarios ranging from object manipulation to legged-robot locomotion. However, the zero-volume nature of manifolds makes the CMP problem challenging, and the state-of-the-art methods still take several seconds to find a path and require a computationally expansive path dataset for imitation learning. Recently, physics-informed motion planning methods have emerged that directly solve the Eikonal equation through neural networks for motion planning and do not require expert demonstrations for learning. Inspired by these approaches, we propose the first physics-informed CMP framework that solves the Eikonal equation on the constraint manifolds and trains neural function for CMP without expert data. Our results show that the proposed approach efficiently solves various CMP problems in both simulation and real-world, including object manipulation under orientation constraints and door opening with a high-dimensional 6-DOF robot manipulator. In these complex settings, our method exhibits high success rates and finds paths in sub-seconds, which is many times faster than the state-of-the-art CMP methods.
|
[
"['Ruiqi Ni' 'Ahmed H. Qureshi']"
] |
null | null |
2403.05767
| null | null |
http://arxiv.org/pdf/2403.05767v1
|
2024-03-09T02:30:04Z
|
2024-03-09T02:30:04Z
|
Extending Activation Steering to Broad Skills and Multiple Behaviours
|
Current large language models have dangerous capabilities, which are likely to become more problematic in the future. Activation steering techniques can be used to reduce risks from these capabilities. In this paper, we investigate the efficacy of activation steering for broad skills and multiple behaviours. First, by comparing the effects of reducing performance on general coding ability and Python-specific ability, we find that steering broader skills is competitive to steering narrower skills. Second, we steer models to become more or less myopic and wealth-seeking, among other behaviours. In our experiments, combining steering vectors for multiple different behaviours into one steering vector is largely unsuccessful. On the other hand, injecting individual steering vectors at different places in a model simultaneously is promising.
|
[
"['Teun van der Weij' 'Massimo Poesio' 'Nandi Schoots']"
] |
null | null |
2403.05778
| null | null |
http://arxiv.org/abs/2403.05778v1
|
2024-03-09T03:21:18Z
|
2024-03-09T03:21:18Z
|
Spatial Clustering Approach for Vessel Path Identification
|
This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation.
|
[
"['Mohamed Abuella' 'M. Amine Atoui' 'Slawomir Nowaczyk' 'Simon Johansson'\n 'Ethan Faghan']"
] |
null | null |
2403.05783
| null | null |
http://arxiv.org/pdf/2403.05783v1
|
2024-03-09T03:33:07Z
|
2024-03-09T03:33:07Z
|
Large Generative Model Assisted 3D Semantic Communication
|
Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.
|
[
"['Feibo Jiang' 'Yubo Peng' 'Li Dong' 'Kezhi Wang' 'Kun Yang' 'Cunhua Pan'\n 'Xiaohu You']"
] |
null | null |
2403.05786
| null | null |
http://arxiv.org/pdf/2403.05786v2
|
2024-05-27T22:07:51Z
|
2024-03-09T04:01:39Z
|
Optimistic Safety for Online Convex Optimization with Unknown Linear
Constraints
|
We study the problem of online convex optimization (OCO) under unknown linear constraints that are either static, or stochastically time-varying. For this problem, we introduce an algorithm that we term Optimistically Safe OCO (OSOCO) and show that it enjoys $tilde{mathcal{O}}(sqrt{T})$ regret and no constraint violation. In the case of static linear constraints, this improves on the previous best known $tilde{mathcal{O}}(T^{2/3})$ regret with only slightly stronger assumptions. In the case of stochastic time-varying constraints, our work supplements existing results that show $mathcal{O}(sqrt{T})$ regret and $mathcal{O}(sqrt{T})$ cumulative violation under more general convex constraints albeit a less general feedback model. In addition to our theoretical guarantees, we also give numerical results comparing the performance of OSOCO to existing algorithms.
|
[
"['Spencer Hutchinson' 'Tianyi Chen' 'Mahnoosh Alizadeh']"
] |
null | null |
2403.05798
| null | null |
http://arxiv.org/pdf/2403.05798v2
|
2024-07-07T19:14:34Z
|
2024-03-09T05:20:48Z
|
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM
for Time Series Forecasting
|
Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, established through the pre-training, is still underexplored and may help yield more distinctive and informative representations to facilitate time series forecasting. To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space. We first design a tokenization module tailored for cross-modality alignment, which explicitly concatenates patches of decomposed time series components to create embeddings that effectively encode the temporal dynamics. Next, we leverage the pre-trained word token embeddings to derive semantic anchors and align selected anchors with time series embeddings by maximizing the cosine similarity in the joint space. This way, $S^2$IP-LLM can retrieve relevant semantic anchors as prompts to provide strong indicators (context) for time series that exhibit different temporal dynamics. With thorough empirical studies on multiple benchmark datasets, we demonstrate that the proposed $S^2$IP-LLM can achieve superior forecasting performance over state-of-the-art baselines. Furthermore, our ablation studies and visualizations verify the necessity of prompt learning informed by semantic space.
|
[
"['Zijie Pan' 'Yushan Jiang' 'Sahil Garg' 'Anderson Schneider'\n 'Yuriy Nevmyvaka' 'Dongjin Song']"
] |
null | null |
2403.05809
| null | null |
http://arxiv.org/pdf/2403.05809v1
|
2024-03-09T06:12:06Z
|
2024-03-09T06:12:06Z
|
Shallow ReLU neural networks and finite elements
|
We point out that (continuous or discontinuous) piecewise linear functions on a convex polytope mesh can be represented by two-hidden-layer ReLU neural networks in a weak sense. In addition, the numbers of neurons of the two hidden layers required to weakly represent are accurately given based on the numbers of polytopes and hyperplanes involved in this mesh. The results naturally hold for constant and linear finite element functions. Such weak representation establishes a bridge between shallow ReLU neural networks and finite element functions, and leads to a perspective for analyzing approximation capability of ReLU neural networks in $L^p$ norm via finite element functions. Moreover, we discuss the strict representation for tensor finite element functions via the recent tensor neural networks.
|
[
"['Pengzhan Jin']"
] |
null | null |
2403.05811
| null | null |
http://arxiv.org/pdf/2403.05811v2
|
2024-03-14T09:24:51Z
|
2024-03-09T06:19:53Z
|
Near Minimax-Optimal Distributional Temporal Difference Algorithms and
The Freedman Inequality in Hilbert Spaces
|
Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One of the core tasks in the field of DRL is distributional policy evaluation, which involves estimating the return distribution $eta^pi$ for a given policy $pi$. The distributional temporal difference (TD) algorithm has been accordingly proposed, which is an extension of the temporal difference algorithm in the classic RL literature. In the tabular case, citet{rowland2018analysis} and citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively. In this paper, we go a step further and analyze the finite-sample performance of distributional TD. To facilitate theoretical analysis, we propose a non-parametric distributional TD algorithm (NTD). For a $gamma$-discounted infinite-horizon tabular Markov decision process, we show that for NTD we need $tilde{O}left(frac{1}{varepsilon^{2p}(1-gamma)^{2p+1}}right)$ iterations to achieve an $varepsilon$-optimal estimator with high probability, when the estimation error is measured by the $p$-Wasserstein distance. This sample complexity bound is minimax optimal (up to logarithmic factors) in the case of the $1$-Wasserstein distance. To achieve this, we establish a novel Freedman's inequality in Hilbert spaces, which would be of independent interest. In addition, we revisit CTD, showing that the same non-asymptotic convergence bounds hold for CTD in the case of the $p$-Wasserstein distance.
|
[
"['Yang Peng' 'Liangyu Zhang' 'Zhihua Zhang']"
] |
null | null |
2403.05818
| null | null |
http://arxiv.org/pdf/2403.05818v2
|
2024-03-12T08:55:00Z
|
2024-03-09T06:58:21Z
|
PR-NET: Leveraging Pathway Refined Network Structures for Prostate
Cancer Patient Condition Prediction
|
The diagnosis and monitoring of Castrate Resistant Prostate Cancer (CRPC) are crucial for cancer patients, but the current models (such as P-NET) have limitations in terms of parameter count, generalization, and cost. To address the issue, we develop a more accurate and efficient Prostate Cancer patient condition prediction model, named PR-NET. By compressing and optimizing the network structure of P-NET, the model complexity is reduced while maintaining high accuracy and interpretability. The PR-NET demonstrated superior performance in predicting prostate cancer patient outcomes, outshining P-NET and six other traditional models with a significant margin. In our rigorous evaluation, PR-NET not only achieved impressive average AUC and Recall scores of 0.94 and 0.83, respectively, on known data but also maintained robust generalizability on five unknown datasets with a higher average AUC of 0.73 and Recall of 0.72, compared to P-NET's 0.68 and 0.5. PR-NET's efficiency was evidenced by its shorter average training and inference times, and its gene-level analysis revealed 46 key genes, demonstrating its enhanced predictive power and efficiency in identifying critical biomarkers for prostate cancer. Future research can further expand its application domains and optimize the model's performance and reliability.
|
[
"['R. Li' 'J. Liu' 'X. L. Deng' 'X. Liu' 'J. C. Guo' 'W. Y. Wu' 'L. Yang']"
] |
null | null |
2403.05821
| null | null |
http://arxiv.org/pdf/2403.05821v1
|
2024-03-09T07:01:44Z
|
2024-03-09T07:01:44Z
|
Optimizing LLM Queries in Relational Workloads
|
Analytical database providers (e.g., Redshift, Databricks, BigQuery) have rapidly added support for invoking Large Language Models (LLMs) through native user-defined functions (UDFs) to help users perform natural language tasks, such as classification, entity extraction, and translation, inside analytical workloads. For instance, an analyst might want to extract customer sentiments on millions of product reviews. However, LLM inference is highly expensive in both computational and economic terms: for example, an NVIDIA L4 GPU running Llama2-7B can only process 6 KB of text per second. In this paper, we explore how to optimize LLM inference for analytical workloads that invoke LLMs within relational queries. We show that relational queries present novel opportunities for accelerating LLM inference, including reordering rows to maximize key-value (KV) cache reuse within the LLM inference engine, reordering columns within a row to further increase cache reuse, and deduplicating redundant inference requests. We implement these optimizations in Apache Spark, with vLLM as the model serving backend and achieve up to 4.4x improvement in end-to-end latency on a benchmark of diverse LLM-based queries on real datasets. To the best of our knowledge, this is the first work to explicitly address the problem of optimizing LLM invocations within SQL queries.
|
[
"['Shu Liu' 'Asim Biswal' 'Audrey Cheng' 'Xiangxi Mo' 'Shiyi Cao'\n 'Joseph E. Gonzalez' 'Ion Stoica' 'Matei Zaharia']"
] |
null | null |
2403.05822
| null | null |
http://arxiv.org/pdf/2403.05822v2
|
2024-03-18T05:11:22Z
|
2024-03-09T07:19:37Z
|
TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic
Analysis and Generation
|
Over the years, network traffic analysis and generation have advanced significantly. From traditional statistical methods, the field has progressed to sophisticated deep learning techniques. This progress has improved the ability to detect complex patterns and security threats, as well as to test and optimize network performance. However, obstacles persist, such as the dependence on labeled data for analysis and the difficulty of generating traffic samples that follow realistic patterns. Pre-trained deep neural networks have emerged as powerful tools to resolve these issues, offering improved performance by learning robust data representations from large unlabeled datasets. Despite their benefits, existing pre-trained models face challenges like token length limitation, which restricts their usefulness in comprehensive traffic analysis and realistic traffic generation. To address these challenges, we introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. This model uses generative pre-training with the linear attention mechanism, which allows for a substantially increased capacity of up to 12,032 tokens from the previous limit of only 512 tokens. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. In generation tasks, it closely resembles real traffic flows, with low JS divergence and an F1 score close to 0.5 (representing a random guess) in discriminating generated data. These advancements hold promise for future applications in both traffic flow classification and generation tasks.
|
[
"['Jian Qu' 'Xiaobo Ma' 'Jianfeng Li']"
] |
null | null |
2403.05848
| null | null |
http://arxiv.org/pdf/2403.05848v2
|
2024-03-22T01:01:58Z
|
2024-03-09T09:17:23Z
|
tLaSDI: Thermodynamics-informed latent space dynamics identification
|
We propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.
|
[
"['Jun Sur Richard Park' 'Siu Wun Cheung' 'Youngsoo Choi' 'Yeonjong Shin']"
] |
null | null |
2403.05864
| null | null |
http://arxiv.org/pdf/2403.05864v1
|
2024-03-09T10:24:12Z
|
2024-03-09T10:24:12Z
|
PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for
Reinforcement Learning in Human-in-the-Loop Systems
|
Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions. However, integrating RL in such settings raises significant privacy concerns, as it might inadvertently expose sensitive user information. Addressing this, our paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy through exploiting an early-exit approach designed explicitly for privacy preservation in HITL environments. This approach dynamically adjusts the tradeoff between privacy protection and system utility, tailoring its operation to individual behavioral patterns and preferences. We mainly highlight the challenge of dealing with the variable and evolving nature of human behavior, which renders static privacy models ineffective. PAPER-HILT's effectiveness is evaluated through its application in two distinct contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms. The empirical results demonstrate PAPER-HILT's capability to provide a personalized equilibrium between user privacy and application utility, adapting effectively to individual user needs and preferences. On average for both experiments, utility (performance) drops by 24%, and privacy (state prediction) improves by 31%.
|
[
"['Mojtaba Taherisadr' 'Salma Elmalaki']"
] |
null | null |
2403.05873
| null | null |
http://arxiv.org/pdf/2403.05873v1
|
2024-03-09T10:49:31Z
|
2024-03-09T10:49:31Z
|
LEGION: Harnessing Pre-trained Language Models for GitHub Topic
Recommendations with Distribution-Balance Loss
|
Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.
|
[
"['Yen-Trang Dang' 'Thanh-Le Cong' 'Phuc-Thanh Nguyen' 'Anh M. T. Bui'\n 'Phuong T. Nguyen' 'Bach Le' 'Quyet-Thang Huynh']"
] |
null | null |
2403.05879
| null | null |
http://arxiv.org/pdf/2403.05879v1
|
2024-03-09T11:09:45Z
|
2024-03-09T11:09:45Z
|
Deep Learning based acoustic measurement approach for robotic
applications on orthopedics
|
In Total Knee Replacement Arthroplasty (TKA), surgical robotics can provide image-guided navigation to fit implants with high precision. Its tracking approach highly relies on inserting bone pins into the bones tracked by the optical tracking system. This is normally done by invasive, radiative manners (implantable markers and CT scans), which introduce unnecessary trauma and prolong the preparation time for patients. To tackle this issue, ultrasound-based bone tracking could offer an alternative. In this study, we proposed a novel deep learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US). We first obtained a set of ultrasound dataset from the cadaver experiment, where the ground truth locations of bones were calculated using bone pins. These data were used to train the proposed CasAtt-UNet to predict bone location automatically and robustly. The ground truth bone locations and those locations of US were recorded simultaneously. Therefore, we could label bone peaks in the raw US signals. As a result, our method achieved sub millimeter precision across all eight bone areas with the only exception of one channel in the ankle. This method enables the robust measurement of lower extremity bone positions from 1D raw ultrasound signals. It shows great potential to apply A-mode ultrasound in orthopedic surgery from safe, convenient, and efficient perspectives.
|
[
"['Bangyu Lan' 'Momen Abayazid' 'Nico Verdonschot' 'Stefano Stramigioli'\n 'Kenan Niu']"
] |
null | null |
2403.05882
| null | null |
http://arxiv.org/pdf/2403.05882v1
|
2024-03-09T11:24:34Z
|
2024-03-09T11:24:34Z
|
DiffRed: Dimensionality Reduction guided by stable rank
|
In this work, we propose a novel dimensionality reduction technique, DiffRed, which first projects the data matrix, A, along first $k_1$ principal components and the residual matrix $A^{*}$ (left after subtracting its $k_1$-rank approximation) along $k_2$ Gaussian random vectors. We evaluate M1, the distortion of mean-squared pair-wise distance, and Stress, the normalized value of RMS of distortion of the pairwise distances. We rigorously prove that DiffRed achieves a general upper bound of $Oleft(sqrt{frac{1-p}{k_2}}right)$ on Stress and $Oleft(frac{(1-p)}{sqrt{k_2*rho(A^{*})}}right)$ on M1 where $p$ is the fraction of variance explained by the first $k_1$ principal components and $rho(A^{*})$ is the stable rank of $A^{*}$. These bounds are tighter than the currently known results for Random maps. Our extensive experiments on a variety of real-world datasets demonstrate that DiffRed achieves near zero M1 and much lower values of Stress as compared to the well-known dimensionality reduction techniques. In particular, DiffRed can map a 6 million dimensional dataset to 10 dimensions with 54% lower Stress than PCA.
|
[
"['Prarabdh Shukla' 'Gagan Raj Gupta' 'Kunal Dutta']"
] |
null | null |
2403.05890
| null | null |
http://arxiv.org/pdf/2403.05890v3
|
2024-06-03T08:14:56Z
|
2024-03-09T12:04:56Z
|
Towards Efficient Replay in Federated Incremental Learning
|
In Federated Learning (FL), the data in each client is typically assumed fixed or static. However, data often comes in an incremental manner in real-world applications, where the data domain may increase dynamically. In this work, we study catastrophic forgetting with data heterogeneity in Federated Incremental Learning (FIL) scenarios where edge clients may lack enough storage space to retain full data. We propose to employ a simple, generic framework for FIL named Re-Fed, which can coordinate each client to cache important samples for replay. More specifically, when a new task arrives, each client first caches selected previous samples based on their global and local importance. Then, the client trains the local model with both the cached samples and the samples from the new task. Theoretically, we analyze the ability of Re-Fed to discover important samples for replay thus alleviating the catastrophic forgetting problem. Moreover, we empirically show that Re-Fed achieves competitive performance compared to state-of-the-art methods.
|
[
"['Yichen Li' 'Qunwei Li' 'Haozhao Wang' 'Ruixuan Li' 'Wenliang Zhong'\n 'Guannan Zhang']"
] |
null | null |
2403.05899
| null | null |
http://arxiv.org/pdf/2403.05899v1
|
2024-03-09T12:33:09Z
|
2024-03-09T12:33:09Z
|
Online Identification of Stochastic Continuous-Time Wiener Models Using
Sampled Data
|
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process.
|
[
"['Mohamed Abdalmoaty' 'Efe C. Balta' 'John Lygeros' 'Roy S. Smith']"
] |
null | null |
2403.05918
| null | null |
http://arxiv.org/pdf/2403.05918v2
|
2024-03-12T02:45:48Z
|
2024-03-09T14:01:04Z
|
SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to
Imbalanced Data
|
In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. In order to balance the data distribution before model training, oversampling methods are often used to generate data for a small number of classes to solve the problem of classifying unbalanced data. Most of the classical oversampling methods are based on the SMOTE technique, which only focuses on the local information of the data, and therefore the generated data may have the problem of not being realistic enough. In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise. problem of poor noise removal. In order to overcome the above problems, we propose a novel oversampling method SEMRes-DDPM.In the SEMRes-DDPM backward diffusion process, a new neural network structure SEMST-ResNet is used, which is suitable for tabular data and has good noise removal effect, and it can generate tabular data with higher quality. Experiments show that the SEMResNet network removes noise better than MLP; SEMRes-DDPM generates data distributions that are closer to the real data distributions than TabDDPM with CWGAN-GP; on 20 real unbalanced tabular datasets with 9 classification models, SEMRes-DDPM improves the quality of the generated tabular data in terms of three evaluation metrics (F1, G-mean, AUC) with better classification performance than other SOTA oversampling methods.
|
[
"['Ming Zheng' 'Yang Yang' 'Zhi-Hang Zhao' 'Shan-Chao Gan' 'Yang Chen'\n 'Si-Kai Ni' 'Yang Lu']"
] |
null | null |
2403.05931
| null | null |
http://arxiv.org/pdf/2403.05931v1
|
2024-03-09T14:50:20Z
|
2024-03-09T14:50:20Z
|
Thread Detection and Response Generation using Transformers with Prompt
Optimisation
|
Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models.
|
[
"['Kevin Joshua T' 'Arnav Agarwal' 'Shriya Sanjay' 'Yash Sarda'\n 'John Sahaya Rani Alex' 'Saurav Gupta' 'Sushant Kumar'\n 'Vishwanath Kamath']"
] |
null | null |
2403.05949
| null | null |
http://arxiv.org/pdf/2403.05949v3
|
2024-04-12T22:30:54Z
|
2024-03-09T16:02:46Z
|
General surgery vision transformer: A video pre-trained foundation model
for general surgery
|
The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest dataset of general surgery videos to-date, consisting of 680 hours of surgical videos, including data from robotic and laparoscopic techniques across 28 procedures; (ii) we propose a technique for video pre-training a general surgery vision transformer (GSViT) on surgical videos based on forward video prediction that can run in real-time for surgical applications, toward which we open-source the code and weights of GSViT; (iii) we also release code and weights for procedure-specific fine-tuned versions of GSViT across 10 procedures; (iv) we demonstrate the performance of GSViT on the Cholec80 phase annotation task, displaying improved performance over state-of-the-art single frame predictors.
|
[
"['Samuel Schmidgall' 'Ji Woong Kim' 'Jeffrey Jopling' 'Axel Krieger']"
] |
null | null |
2403.05963
| null | null |
http://arxiv.org/pdf/2403.05963v3
|
2024-06-02T01:35:28Z
|
2024-03-09T17:05:43Z
|
Robust Emotion Recognition in Context Debiasing
|
Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.
|
[
"['Dingkang Yang' 'Kun Yang' 'Mingcheng Li' 'Shunli Wang' 'Shuaibing Wang'\n 'Lihua Zhang']"
] |
null | null |
2403.05966
| null | null |
http://arxiv.org/pdf/2403.05966v2
|
2024-05-27T13:49:10Z
|
2024-03-09T17:17:07Z
|
Can Generative Models Improve Self-Supervised Representation Learning?
|
The rapid advancement in self-supervised learning (SSL) has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the existing SSL techniques, particularly those employing different augmentations of the same image, often rely on a limited set of simple transformations that are not representative of real-world data variations. This constrains the diversity and quality of samples, which leads to sub-optimal representations. In this paper, we introduce a novel framework that enriches the SSL paradigm by utilizing generative models to produce semantically consistent image augmentations. By directly conditioning generative models on a source image representation, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for self-supervised learning. Our extensive experimental results on various SSL methods demonstrate that our framework significantly enhances the quality of learned visual representations by up to 10% Top-1 accuracy in downstream tasks. This research demonstrates that incorporating generative models into the SSL workflow opens new avenues for exploring the potential of synthetic data. This development paves the way for more robust and versatile representation learning techniques.
|
[
"['Sana Ayromlou' 'Arash Afkanpour' 'Vahid Reza Khazaie'\n 'Fereshteh Forghani']"
] |
null | null |
2403.05973
| null | null |
http://arxiv.org/pdf/2403.05973v1
|
2024-03-09T17:46:24Z
|
2024-03-09T17:46:24Z
|
Calibrating Large Language Models Using Their Generations Only
|
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a challenge. We propose APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or adjusting the given answer based on the confidence. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.
|
[
"['Dennis Ulmer' 'Martin Gubri' 'Hwaran Lee' 'Sangdoo Yun' 'Seong Joon Oh']"
] |
null | null |
2403.05979
| null | null |
http://arxiv.org/pdf/2403.05979v1
|
2024-03-09T18:34:59Z
|
2024-03-09T18:34:59Z
|
Enhancing Classification Performance via Reinforcement Learning for
Feature Selection
|
Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA learning, this paper addresses the feature selection challenge. Using the Breast Cancer Coimbra dataset (BCCDS) and three normalization methods (Min-Max, l1, and l2), the study evaluates the performance of these algorithms. Results show that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies, reaching 87% and 88%, respectively. This highlights the effectiveness of RL-based feature selection methods in optimizing classification tasks, contributing to improved model accuracy and efficiency.
|
[
"['Younes Ghazagh Jahed' 'Seyyed Ali Sadat Tavana']"
] |
null | null |
2403.05996
| null | null |
http://arxiv.org/pdf/2403.05996v2
|
2024-07-15T17:08:06Z
|
2024-03-09T19:56:40Z
|
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence
|
We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence. Under large update-to-data ratios, a recent study by Nikishin et al. (2022) suggested the emergence of a primacy bias, in which agents overfit early interactions and downplay later experience, impairing their ability to learn. In this work, we investigate the phenomena leading to the primacy bias. We inspect the early stages of training that were conjectured to cause the failure to learn and find that one fundamental challenge is a long-standing acquaintance: value function divergence. Overinflated Q-values are found not only on out-of-distribution but also in-distribution data and can be linked to overestimation on unseen action prediction propelled by optimizer momentum. We employ a simple unit-ball normalization that enables learning under large update ratios, show its efficacy on the widely used dm_control suite, and obtain strong performance on the challenging dog tasks, competitive with model-based approaches. Our results question, in parts, the prior explanation for sub-optimal learning due to overfitting early data.
|
[
"['Marcel Hussing' 'Claas Voelcker' 'Igor Gilitschenski'\n 'Amir-massoud Farahmand' 'Eric Eaton']"
] |
null | null |
2403.06003
| null | null |
http://arxiv.org/pdf/2403.06003v1
|
2024-03-09T20:32:17Z
|
2024-03-09T20:32:17Z
|
A Generalized Acquisition Function for Preference-based Reward Learning
|
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that actively synthesizing preference queries to maximize information gain about the reward function parameters improves data efficiency. The information gain criterion focuses on precisely identifying all parameters of the reward function. This can potentially be wasteful as many parameters may result in the same reward, and many rewards may result in the same behavior in the downstream tasks. Instead, we show that it is possible to optimize for learning the reward function up to a behavioral equivalence class, such as inducing the same ranking over behaviors, distribution over choices, or other related definitions of what makes two rewards similar. We introduce a tractable framework that can capture such definitions of similarity. Our experiments in a synthetic environment, an assistive robotics environment with domain transfer, and a natural language processing problem with real datasets demonstrate the superior performance of our querying method over the state-of-the-art information gain method.
|
[
"['Evan Ellis' 'Gaurav R. Ghosal' 'Stuart J. Russell' 'Anca Dragan'\n 'Erdem Bıyık']"
] |
null | null |
2403.06009
| null | null |
http://arxiv.org/pdf/2403.06009v2
|
2024-06-13T15:31:28Z
|
2024-03-09T21:07:16Z
|
Detectors for Safe and Reliable LLMs: Implementations, Uses, and
Limitations
|
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be feasible to impose direct safety constraints on a deployed model. Therefore, an efficient and reliable alternative is required. To this end, we present our ongoing efforts to create and deploy a library of detectors: compact and easy-to-build classification models that provide labels for various harms. In addition to the detectors themselves, we discuss a wide range of uses for these detector models - from acting as guardrails to enabling effective AI governance. We also deep dive into inherent challenges in their development and discuss future work aimed at making the detectors more reliable and broadening their scope.
|
[
"['Swapnaja Achintalwar' 'Adriana Alvarado Garcia' 'Ateret Anaby-Tavor'\n 'Ioana Baldini' 'Sara E. Berger' 'Bishwaranjan Bhattacharjee'\n 'Djallel Bouneffouf' 'Subhajit Chaudhury' 'Pin-Yu Chen' 'Lamogha Chiazor'\n 'Elizabeth M. Daly' 'Kirushikesh DB' 'Rogério Abreu de Paula'\n 'Pierre Dognin' 'Eitan Farchi' 'Soumya Ghosh' 'Michael Hind'\n 'Raya Horesh' 'George Kour' 'Ja Young Lee' 'Nishtha Madaan'\n 'Sameep Mehta' 'Erik Miehling' 'Keerthiram Murugesan' 'Manish Nagireddy'\n 'Inkit Padhi' 'David Piorkowski' 'Ambrish Rawat' 'Orna Raz'\n 'Prasanna Sattigeri' 'Hendrik Strobelt' 'Sarathkrishna Swaminathan'\n 'Christoph Tillmann' 'Aashka Trivedi' 'Kush R. Varshney' 'Dennis Wei'\n 'Shalisha Witherspooon' 'Marcel Zalmanovici']"
] |
null | null |
2403.06011
| null | null |
http://arxiv.org/pdf/2403.06011v1
|
2024-03-09T21:10:10Z
|
2024-03-09T21:10:10Z
|
Reinforcement Learning Paycheck Optimization for Multivariate Financial
Goals
|
We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings.
|
[
"['Melda Alaluf' 'Giulia Crippa' 'Sinong Geng' 'Zijian Jing'\n 'Nikhil Krishnan' 'Sanjeev Kulkarni' 'Wyatt Navarro' 'Ronnie Sircar'\n 'Jonathan Tang']"
] |
null | null |
2403.06013
| null | null |
http://arxiv.org/pdf/2403.06013v1
|
2024-03-09T21:26:10Z
|
2024-03-09T21:26:10Z
|
Are Classification Robustness and Explanation Robustness Really Strongly
Correlated? An Analysis Through Input Loss Landscape
|
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated. Through a novel evaluation approach leveraging clustering for efficient assessment of explanation robustness, we demonstrate that enhancing explanation robustness does not necessarily flatten the input loss landscape with respect to explanation loss - contrary to flattened loss landscapes indicating better classification robustness. To deeply investigate this contradiction, a groundbreaking training method designed to adjust the loss landscape with respect to explanation loss is proposed. Through the new training method, we uncover that although such adjustments can impact the robustness of explanations, they do not have an influence on the robustness of classification. These findings not only challenge the prevailing assumption of a strong correlation between the two forms of robustness but also pave new pathways for understanding relationship between loss landscape and explanation loss.
|
[
"['Tiejin Chen' 'Wenwang Huang' 'Linsey Pang' 'Dongsheng Luo' 'Hua Wei']"
] |
null | null |
2403.06014
| null | null |
http://arxiv.org/pdf/2403.06014v1
|
2024-03-09T21:26:22Z
|
2024-03-09T21:26:22Z
|
Hard-label based Small Query Black-box Adversarial Attack
|
We consider the hard label based black box adversarial attack setting which solely observes predicted classes from the target model. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful attack. One approach to tackle this drawback is utilising the adversarial transferability between white box surrogate models and black box target model. However, the majority of the methods adopting this approach are soft label based to take the full advantage of zeroth order optimisation. Unlike mainstream methods, we propose a new practical setting of hard label based attack with an optimisation process guided by a pretrained surrogate model. Experiments show the proposed method significantly improves the query efficiency of the hard label based black-box attack across various target model architectures. We find the proposed method achieves approximately 5 times higher attack success rate compared to the benchmarks, especially at the small query budgets as 100 and 250.
|
[
"['Jeonghwan Park' 'Paul Miller' 'Niall McLaughlin']"
] |
null | null |
2403.06015
| null | null |
http://arxiv.org/pdf/2403.06015v1
|
2024-03-09T21:29:25Z
|
2024-03-09T21:29:25Z
|
Grafting: Making Random Forests Consistent
|
Despite their performance and widespread use, little is known about the theory of Random Forests. A major unanswered question is whether, or when, the Random Forest algorithm is consistent. The literature explores various variants of the classic Random Forest algorithm to address this question and known short-comings of the method. This paper is a contribution to this literature. Specifically, the suitability of grafting consistent estimators onto a shallow CART is explored. It is shown that this approach has a consistency guarantee and performs well in empirical settings.
|
[
"['Nicholas Waltz']"
] |
null | null |
2403.06017
| null | null |
http://arxiv.org/pdf/2403.06017v2
|
2024-06-18T03:55:04Z
|
2024-03-09T21:33:26Z
|
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New
Benchmark
|
Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies on poorly constructed semi-synthetic datasets or substandard real-world datasets. In such cases, even a basic Multilayer Perceptron (MLP) can outperform Graph Neural Networks (GNNs) in both utility and fairness. In this work, we illustrate that many datasets fail to provide meaningful information in the edges, which may challenge the necessity of using graph structures in these problems. To address these issues, we develop and introduce a collection of synthetic, semi-synthetic, and real-world datasets that fulfill a broad spectrum of requirements. These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models. The proposed synthetic and semi-synthetic datasets offer the flexibility to create data with controllable bias parameters, thereby enabling the generation of desired datasets with user-defined bias values with ease. Moreover, we conduct systematic evaluations of these proposed datasets and establish a unified evaluation approach for fair graph learning models. Our extensive experimental results with fair graph learning methods across our datasets demonstrate their effectiveness in benchmarking the performance of these methods. Our datasets and the code for reproducing our experiments are available at https://github.com/XweiQ/Benchmark-GraphFairness.
|
[
"['Xiaowei Qian' 'Zhimeng Guo' 'Jialiang Li' 'Haitao Mao' 'Bingheng Li'\n 'Suhang Wang' 'Yao Ma']"
] |
null | null |
2403.06018
| null | null |
http://arxiv.org/pdf/2403.06018v1
|
2024-03-09T21:36:13Z
|
2024-03-09T21:36:13Z
|
Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in
Low-Resource Languages
|
Large pre-trained language models (PLMs) are at the forefront of advances in Natural Language Processing. One widespread use case of PLMs is "prompting" - or in-context learning - where a user provides a description of a task and some completed examples of the task to a PLM as context before prompting the PLM to perform the task on a new example. Only the largest, most capable PLMs are able to perform in-context learning effectively, and these models are typically trained with a predominantly English corpus, leaving all other languages behind. The data limitations in most languages preclude the training of language-specific PLMs capable of prompting. Albeit the surge in work of prompting settings, it is still unclear how PLMs should be adapted cross-lingually specifically for prompting. We evaluate the possible methods to adapt LLaMa, a 7B parameter open-source PLM mainly trained in English, for prompting in low-resource languages, namely for Kinyarwanda, Hausa, and Luganda. We consider three methods: few-shot prompting (prompt), language-adaptive fine-tuning (LAFT), and neural machine translation (translate), and evaluate on abstractive summarization, multi-class topic classification, and named-entity recognition. Although LAFT carries the greatest compute cost and intuitively should lead to the best results, our experiments exhibit that LAFT is only occasionally the optimal choice for adapting PLMs for prompting. Rather, the translate and prompt settings are a compute-efficient and cost-effective method of few-shot prompting for the selected low-resource languages. We find that the results are task and language dependent but find that the prompting method is the best on average across all tasks and languages. Results show that the prompt setting performs better than both translating and LAFT with statistical significance for all shots when aggregated across all tasks and languages.
|
[
"['Christopher Toukmaji']"
] |
null | null |
2403.06020
| null | null |
http://arxiv.org/pdf/2403.06020v2
|
2024-03-22T13:51:55Z
|
2024-03-09T21:45:31Z
|
Multi-conditioned Graph Diffusion for Neural Architecture Search
|
Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures. We then propose a multi-conditioned classifier-free guidance approach applied to graph diffusion networks to jointly impose constraints such as high accuracy and low hardware latency. Unlike the related work, our method is completely differentiable and requires only a single model training. In our evaluations, we show promising results on six standard benchmarks, yielding novel and unique architectures at a fast speed, i.e. less than 0.2 seconds per architecture. Furthermore, we demonstrate the generalisability and efficiency of our method through experiments on ImageNet dataset.
|
[
"['Rohan Asthana' 'Joschua Conrad' 'Youssef Dawoud' 'Maurits Ortmanns'\n 'Vasileios Belagiannis']"
] |
null | null |
2403.06021
| null | null |
http://arxiv.org/pdf/2403.06021v1
|
2024-03-09T21:55:55Z
|
2024-03-09T21:55:55Z
|
Hierarchical Query Classification in E-commerce Search
|
E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The significance of this task is amplified when dealing with sensitive query categorization or critical information dissemination, where inaccuracies can lead to considerable negative impacts. The inherent complexity of hierarchical query classification is compounded by two primary challenges: (1) the pronounced class imbalance that skews towards dominant categories, and (2) the inherent brevity and ambiguity of search queries that hinder accurate classification. To address these challenges, we introduce a novel framework that leverages hierarchical information through (i) enhanced representation learning that utilizes the contrastive loss to discern fine-grained instance relationships within the hierarchy, called ''instance hierarchy'', and (ii) a nuanced hierarchical classification loss that attends to the intrinsic label taxonomy, named ''label hierarchy''. Additionally, based on our observation that certain unlabeled queries share typographical similarities with labeled queries, we propose a neighborhood-aware sampling technique to intelligently select these unlabeled queries to boost the classification performance. Extensive experiments demonstrate that our proposed method is better than state-of-the-art (SOTA) on the proprietary Amazon dataset, and comparable to SOTA on the public datasets of Web of Science and RCV1-V2. These results underscore the efficacy of our proposed solution, and pave the path toward the next generation of hierarchy-aware query classification systems.
|
[
"['Bing He' 'Sreyashi Nag' 'Limeng Cui' 'Suhang Wang' 'Zheng Li'\n 'Rahul Goutam' 'Zhen Li' 'Haiyang Zhang']"
] |
null | null |
2403.06023
| null | null |
http://arxiv.org/pdf/2403.06023v1
|
2024-03-09T22:18:26Z
|
2024-03-09T22:18:26Z
|
Persian Slang Text Conversion to Formal and Deep Learning of Persian
Short Texts on Social Media for Sentiment Classification
|
The lack of a suitable tool for the analysis of conversational texts in the Persian language has made various analyses of these texts, including Sentiment Analysis, difficult. In this research, we tried to make the understanding of these texts easier for the machine by providing PSC, Persian Slang Converter, a tool for converting conversational texts into formal ones, and by using the most up-to-date and best deep learning methods along with the PSC, the sentiment learning of short Persian language texts for the machine in a better way. be made More than 10 million unlabeled texts from various social networks and movie subtitles (as Conversational texts) and about 10 million news texts (as formal texts) have been used for training unsupervised models and formal implementation of the tool. 60,000 texts from the comments of Instagram social network users with positive, negative, and neutral labels are considered supervised data for training the emotion classification model of short texts. Using the formal tool, 57% of the words of the corpus of conversation were converted. Finally, by using the formalizer, FastText model, and deep LSTM network, an accuracy of 81.91 was obtained on the test data.
|
[
"['Mohsen Khazeni' 'Mohammad Heydari' 'Amir Albadvi']"
] |
null | null |
2403.06024
| null | null |
http://arxiv.org/pdf/2403.06024v1
|
2024-03-09T22:23:45Z
|
2024-03-09T22:23:45Z
|
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis
Diagnosis
|
Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. When deployed, SMMIL can combine information from two input modalities, spectral Dopplers and 2D cineloops, to produce a study-level AS diagnosis. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both modalities to improve its classifier. Experiments demonstrate that SMMIL outperforms recent alternatives at 3-level AS severity classification as well as several clinically relevant AS detection tasks.
|
[
"['Zhe Huang' 'Xiaowei Yu' 'Benjamin S. Wessler' 'Michael C. Hughes']"
] |
null | null |
2403.06026
| null | null |
http://arxiv.org/pdf/2403.06026v2
|
2024-03-13T00:09:46Z
|
2024-03-09T22:28:46Z
|
Towards a Generic Representation of Combinatorial Problems for
Learning-Based Approaches
|
In recent years, there has been a growing interest in using learning-based approaches for solving combinatorial problems, either in an end-to-end manner or in conjunction with traditional optimization algorithms. In both scenarios, the challenge lies in encoding the targeted combinatorial problems into a structure compatible with the learning algorithm. Many existing works have proposed problem-specific representations, often in the form of a graph, to leverage the advantages of textit{graph neural networks}. However, these approaches lack generality, as the representation cannot be easily transferred from one combinatorial problem to another one. While some attempts have been made to bridge this gap, they still offer a partial generality only. In response to this challenge, this paper advocates for progress toward a fully generic representation of combinatorial problems for learning-based approaches. The approach we propose involves constructing a graph by breaking down any constraint of a combinatorial problem into an abstract syntax tree and expressing relationships (e.g., a variable involved in a constraint) through the edges. Furthermore, we introduce a graph neural network architecture capable of efficiently learning from this representation. The tool provided operates on combinatorial problems expressed in the XCSP3 format, handling all the constraints available in the 2023 mini-track competition. Experimental results on four combinatorial problems demonstrate that our architecture achieves performance comparable to dedicated architectures while maintaining generality. Our code and trained models are publicly available at url{https://github.com/corail-research/learning-generic-csp}.
|
[
"['Léo Boisvert' 'Hélène Verhaeghe' 'Quentin Cappart']"
] |
null | null |
2403.06027
| null | null |
http://arxiv.org/pdf/2403.06027v1
|
2024-03-09T22:29:24Z
|
2024-03-09T22:29:24Z
|
Multimodal deep learning approach to predicting neurological recovery
from coma after cardiac arrest
|
This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac arrest using clinical data and time-series such as multi-channel EEG and ECG signals. Our modelling approach is multimodal, based on two-dimensional spectrogram representations derived from numerous EEG channels, alongside the integration of clinical data and features extracted directly from EEG recordings. Our submitted model achieved a Challenge score of $0.53$ on the hidden test set for predictions made $72$ hours after return of spontaneous circulation. Our study shows the efficacy and limitations of employing transfer learning in medical classification. With regard to prospective implementation, our analysis reveals that the performance of the model is strongly linked to the selection of a decision threshold and exhibits strong variability across data splits.
|
[
"['Felix H. Krones' 'Ben Walker' 'Guy Parsons' 'Terry Lyons' 'Adam Mahdi']"
] |
null | null |
2403.06031
| null | null |
http://arxiv.org/pdf/2403.06031v1
|
2024-03-09T22:41:33Z
|
2024-03-09T22:41:33Z
|
FairTargetSim: An Interactive Simulator for Understanding and Explaining
the Fairness Effects of Target Variable Definition
|
Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications on fairness: biases are often encoded in target variable definition itself, before any data collection or training. We present an interactive simulator, FairTargetSim (FTS), that illustrates how target variable definition impacts fairness. FTS is a valuable tool for algorithm developers, researchers, and non-technical stakeholders. FTS uses a case study of algorithmic hiring, using real-world data and user-defined target variables. FTS is open-source and available at: http://tinyurl.com/ftsinterface. The video accompanying this paper is here: http://tinyurl.com/ijcaifts.
|
[
"['Dalia Gala' 'Milo Phillips-Brown' 'Naman Goel' 'Carinal Prunkl'\n 'Laura Alvarez Jubete' 'medb corcoran' 'Ray Eitel-Porter']"
] |
null | null |
2403.06033
| null | null |
http://arxiv.org/pdf/2403.06033v1
|
2024-03-09T22:49:04Z
|
2024-03-09T22:49:04Z
|
Predicting Depression and Anxiety: A Multi-Layer Perceptron for
Analyzing the Mental Health Impact of COVID-19
|
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.
|
[
"['David Fong' 'Tianshu Chu' 'Matthew Heflin' 'Xiaosi Gu'\n 'Oshani Seneviratne']"
] |
null | null |
2403.06041
| null | null |
http://arxiv.org/pdf/2403.06041v1
|
2024-03-09T23:28:54Z
|
2024-03-09T23:28:54Z
|
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
|
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the other hand, algorithmic data generation methods are usually limited by their model capacities, making them unable to offer realistic and diverse data needed by various application users. In this work, we study trajectory-level data generation for multi-human or human-robot interaction scenarios and propose a learning-based automatic trajectory generation model, which we call Multi-Agent TRajectory generation with dIverse conteXts (MATRIX). MATRIX is capable of generating interactive human behaviors in realistic diverse contexts. We achieve this goal by modeling the explicit and interpretable objectives so that MATRIX can generate human motions based on diverse destinations and heterogeneous behaviors. We carried out extensive comparison and ablation studies to illustrate the effectiveness of our approach across various metrics. We also presented experiments that demonstrate the capability of MATRIX to serve as data augmentation for imitation-based motion planning.
|
[
"['Zhuo Xu' 'Rui Zhou' 'Yida Yin' 'Huidong Gao' 'Masayoshi Tomizuka'\n 'Jiachen Li']"
] |
null | null |
2403.06048
| null | null |
http://arxiv.org/pdf/2403.06048v1
|
2024-03-10T00:07:47Z
|
2024-03-10T00:07:47Z
|
Texture image retrieval using a classification and contourlet-based
features
|
In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the Redundant Contourlet transform that extracts a richer directional information in the image. Moreover, the process of image search is improved through a learning-based approach where the images of the database are classified using an adapted similarity metric to the statistical modeling of the RCT-Plus transform. A query is then first classified to select the best texture class after which the retained class images are ranked to select top ones. By this, we have achieved significant improvements in the retrieval rates compared to previous CBIR schemes.
|
[
"['Asal Rouhafzay' 'Nadia Baaziz' 'Mohand Said Allili']"
] |
null | null |
2403.06054
| null | null |
http://arxiv.org/pdf/2403.06054v5
|
2024-05-29T00:09:08Z
|
2024-03-10T00:47:05Z
|
Decoupled Data Consistency with Diffusion Purification for Image
Restoration
|
Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability to model data distributions. To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models. However, the additional gradient steps pose a challenge for real-world practical applications as they incur a large computational overhead, thereby increasing inference time. They also present additional difficulties when using accelerated diffusion model samplers, as the number of data consistency steps is limited by the number of reverse sampling steps. In this work, we propose a novel diffusion-based image restoration solver that addresses these issues by decoupling the reverse process from the data consistency steps. Our method involves alternating between a reconstruction phase to maintain data consistency and a refinement phase that enforces the prior via diffusion purification. Our approach demonstrates versatility, making it highly adaptable for efficient problem-solving in latent space. Additionally, it reduces the necessity for numerous sampling steps through the integration of consistency models. The efficacy of our approach is validated through comprehensive experiments across various image restoration tasks, including image denoising, deblurring, inpainting, and super-resolution.
|
[
"['Xiang Li' 'Soo Min Kwon' 'Ismail R. Alkhouri' 'Saiprasad Ravishankar'\n 'Qing Qu']"
] |
null | null |
2403.06056
| null | null |
http://arxiv.org/pdf/2403.06056v1
|
2024-03-10T01:07:22Z
|
2024-03-10T01:07:22Z
|
Absence of spurious solutions far from ground truth: A low-rank analysis
with high-order losses
|
Matrix sensing problems exhibit pervasive non-convexity, plaguing optimization with a proliferation of suboptimal spurious solutions. Avoiding convergence to these critical points poses a major challenge. This work provides new theoretical insights that help demystify the intricacies of the non-convex landscape. In this work, we prove that under certain conditions, critical points sufficiently distant from the ground truth matrix exhibit favorable geometry by being strict saddle points rather than troublesome local minima. Moreover, we introduce the notion of higher-order losses for the matrix sensing problem and show that the incorporation of such losses into the objective function amplifies the negative curvature around those distant critical points. This implies that increasing the complexity of the objective function via high-order losses accelerates the escape from such critical points and acts as a desirable alternative to increasing the complexity of the optimization problem via over-parametrization. By elucidating key characteristics of the non-convex optimization landscape, this work makes progress towards a comprehensive framework for tackling broader machine learning objectives plagued by non-convexity.
|
[
"['Ziye Ma' 'Ying Chen' 'Javad Lavaei' 'Somayeh Sojoudi']"
] |
null | null |
2403.06060
| null | null |
http://arxiv.org/pdf/2403.06060v1
|
2024-03-10T01:39:10Z
|
2024-03-10T01:39:10Z
|
Ensemble Language Models for Multilingual Sentiment Analysis
|
The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset. Moreover, We investigated four pretrained language models and proposed two ensemble language models. Our findings include monolingual models exhibiting superior performance and ensemble models outperforming the baseline while the majority voting ensemble outperforms the English language.
|
[
"['Md Arid Hasan']"
] |
null | null |
2403.06064
| null | null |
http://arxiv.org/pdf/2403.06064v3
|
2024-06-14T04:15:20Z
|
2024-03-10T02:16:13Z
|
L^2GC:Lorentzian Linear Graph Convolutional Networks for Node
Classification
|
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to introduce hyperbolic space into linear GCN and propose a novel framework for Lorentzian linear GCN. Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data. Experimental results on standard citation networks datasets with semi-supervised learning show that our approach yields new state-of-the-art results of accuracy 74.7$%$ on Citeseer and 81.3$%$ on PubMed datasets. Furthermore, we observe that our approach can be trained up to two orders of magnitude faster than other nonlinear GCN models on PubMed dataset. Our code is publicly available at https://github.com/llqy123/LLGC-master.
|
[
"['Qiuyu Liang' 'Weihua Wang' 'Feilong Bao' 'Guanglai Gao']"
] |
null | null |
2403.06066
| null | null |
http://arxiv.org/pdf/2403.06066v1
|
2024-03-10T03:04:13Z
|
2024-03-10T03:04:13Z
|
CausalCellSegmenter: Causal Inference inspired Diversified Aggregation
Convolution for Pathology Image Segmentation
|
Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques. The DAC module is designed which incorporates diverse downsampling features through a simple, parameter-free attention module (SimAM), aiming to overcome the problems of false-positive identification and edge blurring. Furthermore, we introduce CIM to leverage sample weighting by directly removing the spurious correlations between features for every input sample and concentrating more on the correlation between features and labels. Extensive experiments on the MoNuSeg-2018 dataset achieves promising results, outperforming other state-of-the-art methods, where the mIoU and DSC scores growing by 3.6% and 2.65%.
|
[
"['Dawei Fan' 'Yifan Gao' 'Jiaming Yu' 'Yanping Chen' 'Wencheng Li'\n 'Chuancong Lin' 'Kaibin Li' 'Changcai Yang' 'Riqing Chen' 'Lifang Wei']"
] |
null | null |
2403.06069
| null | null |
http://arxiv.org/pdf/2403.06069v1
|
2024-03-10T03:22:57Z
|
2024-03-10T03:22:57Z
|
Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and
Denoising
|
Conditional diffusion models have gained recognition for their effectiveness in image restoration tasks, yet their iterative denoising process, starting from Gaussian noise, often leads to slow inference speeds. As a promising alternative, the Image-to-Image Schr"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models. In this study, we extended the I2SB method by introducing the Implicit Image-to-Image Schrodinger Bridge (I3SB), transitioning its generative process to a non-Markovian process by incorporating corrupted images in each generative step. This enhancement empowers I3SB to generate images with better texture restoration using a small number of generative steps. The proposed method was validated on CT super-resolution and denoising tasks and outperformed existing methods, including the conditional denoising diffusion probabilistic model (cDDPM) and I2SB, in both visual quality and quantitative metrics. These findings underscore the potential of I3SB in improving medical image restoration by providing fast and accurate generative modeling.
|
[
"['Yuang Wang' 'Siyeop Yoon' 'Pengfei Jin' 'Matthew Tivnan' 'Zhennong Chen'\n 'Rui Hu' 'Li Zhang' 'Zhiqiang Chen' 'Quanzheng Li' 'Dufan Wu']"
] |
null | null |
2403.06079
| null | null |
http://arxiv.org/pdf/2403.06079v2
|
2024-04-16T03:07:02Z
|
2024-03-10T03:51:59Z
|
Generalization of Graph Neural Networks through the Lens of Homomorphism
|
Despite the celebrated popularity of Graph Neural Networks (GNNs) across numerous applications, the ability of GNNs to generalize remains less explored. In this work, we propose to study the generalization of GNNs through a novel perspective - analyzing the entropy of graph homomorphism. By linking graph homomorphism with information-theoretic measures, we derive generalization bounds for both graph and node classifications. These bounds are capable of capturing subtleties inherent in various graph structures, including but not limited to paths, cycles and cliques. This enables a data-dependent generalization analysis with robust theoretical guarantees. To shed light on the generality of of our proposed bounds, we present a unifying framework that can characterize a broad spectrum of GNN models through the lens of graph homomorphism. We validate the practical applicability of our theoretical findings by showing the alignment between the proposed bounds and the empirically observed generalization gaps over both real-world and synthetic datasets.
|
[
"['Shouheng Li' 'Dongwoo Kim' 'Qing Wang']"
] |
null | null |
2403.06080
| null | null |
http://arxiv.org/pdf/2403.06080v1
|
2024-03-10T03:59:24Z
|
2024-03-10T03:59:24Z
|
Local Vertex Colouring Graph Neural Networks
|
In recent years, there has been a significant amount of research focused on expanding the expressivity of Graph Neural Networks (GNNs) beyond the Weisfeiler-Lehman (1-WL) framework. While many of these studies have yielded advancements in expressivity, they have frequently come at the expense of decreased efficiency or have been restricted to specific types of graphs. In this study, we investigate the expressivity of GNNs from the perspective of graph search. Specifically, we propose a new vertex colouring scheme and demonstrate that classical search algorithms can efficiently compute graph representations that extend beyond the 1-WL. We show the colouring scheme inherits useful properties from graph search that can help solve problems like graph biconnectivity. Furthermore, we show that under certain conditions, the expressivity of GNNs increases hierarchically with the radius of the search neighbourhood. To further investigate the proposed scheme, we develop a new type of GNN based on two search strategies, breadth-first search and depth-first search, highlighting the graph properties they can capture on top of 1-WL. Our code is available at https://github.com/seanli3/lvc.
|
[
"['Shouheng Li' 'Dongwoo Kim' 'Qing Wang']"
] |
null | null |
2403.06082
| null | null |
http://arxiv.org/pdf/2403.06082v1
|
2024-03-10T04:01:49Z
|
2024-03-10T04:01:49Z
|
FrameQuant: Flexible Low-Bit Quantization for Transformers
|
Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To mitigate this difficulty, Post-Training Quantization seeks to modify a pre-trained model and quantize it to eight bits or lower, significantly boosting compute/memory/latency efficiency. Such models have been successfully quantized to four bits with some performance loss. In this work, we outline a simple scheme to quantize Transformer-based models to just two bits (plus some overhead) with only a small drop in accuracy. Key to our formulation is a concept borrowed from Harmonic analysis called Fusion Frames. Our main finding is that the quantization must take place not in the original weight space, but instead in the Fusion Frame representations. If quantization is interpreted as the addition of noise, our casting of the problem allows invoking an extensive body of known consistent recovery and noise robustness guarantees. Further, if desired, de-noising filters are known in closed form. We show empirically, via a variety of experiments, that (almost) two-bit quantization for Transformer models promises sizable efficiency gains.
|
[
"['Harshavardhan Adepu' 'Zhanpeng Zeng' 'Li Zhang' 'Vikas Singh']"
] |
null | null |
2403.06087
| null | null |
http://arxiv.org/pdf/2403.06087v1
|
2024-03-10T04:17:42Z
|
2024-03-10T04:17:42Z
|
Learning the irreversible progression trajectory of Alzheimer's disease
|
Alzheimer's disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset of symptoms. Machine learning (ML) models have been shown effective in predicting the onset of AD. Yet for subjects with follow-up visits, existing techniques for AD classification only aim for accurate group assignment, where the monotonically increasing risk across follow-up visits is usually ignored. Resulted fluctuating risk scores across visits violate the irreversibility of AD, hampering the trustworthiness of models and also providing little value to understanding the disease progression. To address this issue, we propose a novel regularization approach to predict AD longitudinally. Our technique aims to maintain the expected monotonicity of increasing disease risk during progression while preserving expressiveness. Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits. We evaluate our method using the longitudinal structural MRI and amyloid-PET imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our model outperforms existing techniques in capturing the progressiveness of disease risk, and at the same time preserves prediction accuracy.
|
[
"['Yipei Wang' 'Bing He' 'Shannon Risacher' 'Andrew Saykin' 'Jingwen Yan'\n 'Xiaoqian Wang']"
] |
null | null |
2403.06088
| null | null |
http://arxiv.org/pdf/2403.06088v1
|
2024-03-10T04:17:54Z
|
2024-03-10T04:17:54Z
|
Towards In-Vehicle Multi-Task Facial Attribute Recognition:
Investigating Synthetic Data and Vision Foundation Models
|
In the burgeoning field of intelligent transportation systems, enhancing vehicle-driver interaction through facial attribute recognition, such as facial expression, eye gaze, age, etc., is of paramount importance for safety, personalization, and overall user experience. However, the scarcity of comprehensive large-scale, real-world datasets poses a significant challenge for training robust multi-task models. Existing literature often overlooks the potential of synthetic datasets and the comparative efficacy of state-of-the-art vision foundation models in such constrained settings. This paper addresses these gaps by investigating the utility of synthetic datasets for training complex multi-task models that recognize facial attributes of passengers of a vehicle, such as gaze plane, age, and facial expression. Utilizing transfer learning techniques with both pre-trained Vision Transformer (ViT) and Residual Network (ResNet) models, we explore various training and adaptation methods to optimize performance, particularly when data availability is limited. We provide extensive post-evaluation analysis, investigating the effects of synthetic data distributions on model performance in in-distribution data and out-of-distribution inference. Our study unveils counter-intuitive findings, notably the superior performance of ResNet over ViTs in our specific multi-task context, which is attributed to the mismatch in model complexity relative to task complexity. Our results highlight the challenges and opportunities for enhancing the use of synthetic data and vision foundation models in practical applications.
|
[
"['Esmaeil Seraj' 'Walter Talamonti']"
] |
null | null |
2403.06100
| null | null |
http://arxiv.org/pdf/2403.06100v1
|
2024-03-10T05:55:00Z
|
2024-03-10T05:55:00Z
|
Automatic design optimization of preference-based subjective evaluation
with online learning in crowdsourcing environment
|
A preference-based subjective evaluation is a key method for evaluating generative media reliably. However, its huge combinations of pairs prohibit it from being applied to large-scale evaluation using crowdsourcing. To address this issue, we propose an automatic optimization method for preference-based subjective evaluation in terms of pair combination selections and allocation of evaluation volumes with online learning in a crowdsourcing environment. We use a preference-based online learning method based on a sorting algorithm to identify the total order of evaluation targets with minimum sample volumes. Our online learning algorithm supports parallel and asynchronous execution under fixed-budget conditions required for crowdsourcing. Our experiment on preference-based subjective evaluation of synthetic speech shows that our method successfully optimizes the test by reducing pair combinations from 351 to 83 and allocating optimal evaluation volumes for each pair ranging from 30 to 663 without compromising evaluation accuracies and wasting budget allocations.
|
[
"['Yusuke Yasuda' 'Tomoki Toda']"
] |
null | null |
2403.06135
| null | null |
http://arxiv.org/pdf/2403.06135v1
|
2024-03-10T08:50:56Z
|
2024-03-10T08:50:56Z
|
MACE: Mass Concept Erasure in Diffusion Models
|
The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper, we introduce MACE, a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast, MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning, collectively eliminating the information of undesirable concepts. Furthermore, MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure, celebrity erasure, explicit content erasure, and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.
|
[
"['Shilin Lu' 'Zilan Wang' 'Leyang Li' 'Yanzhu Liu' 'Adams Wai-Kin Kong']"
] |
null | null |
2403.06153
| null | null |
http://arxiv.org/pdf/2403.06153v2
|
2024-03-12T23:10:16Z
|
2024-03-10T09:54:56Z
|
The AL$\ell_0$CORE Tensor Decomposition for Sparse Count Data
|
This paper introduces AL$ell_0$CORE, a new form of probabilistic non-negative tensor decomposition. AL$ell_0$CORE is a Tucker decomposition where the number of non-zero elements (i.e., the $ell_0$-norm) of the core tensor is constrained to a preset value $Q$ much smaller than the size of the core. While the user dictates the total budget $Q$, the locations and values of the non-zero elements are latent variables and allocated across the core tensor during inference. AL$ell_0$CORE -- i.e., $allo$cated $ell_0$-$co$nstrained $core$-- thus enjoys both the computational tractability of CP decomposition and the qualitatively appealing latent structure of Tucker. In a suite of real-data experiments, we demonstrate that AL$ell_0$CORE typically requires only tiny fractions (e.g.,~1%) of the full core to achieve the same results as full Tucker decomposition at only a correspondingly tiny fraction of the cost.
|
[
"['John Hood' 'Aaron Schein']"
] |
null | null |
2403.06173
| null | null |
http://arxiv.org/pdf/2403.06173v1
|
2024-03-10T10:58:54Z
|
2024-03-10T10:58:54Z
|
Speeding up 6-DoF Grasp Sampling with Quality-Diversity
|
Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many manipulation tasks. Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem. This paper investigates how QD can be combined with priors to speed up the generation of diverse grasps poses in simulation compared to standard 6-DoF grasp sampling schemes. Experiments conducted on 4 grippers with 2-to-5 fingers on standard objects show that QD outperforms commonly used methods by a large margin. Further experiments show that QD optimization automatically finds some efficient priors that are usually hard coded. The deployment of generated grasps on a 2-finger gripper and an Allegro hand shows that the diversity produced maintains sim-to-real transferability. We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.
|
[
"['Johann Huber' 'François Hélénon' 'Mathilde Kappel' 'Elie Chelly'\n 'Mahdi Khoramshahi' 'Faïz Ben Amar' 'Stéphane Doncieux']"
] |
null | null |
2403.06174
| null | null |
http://arxiv.org/pdf/2403.06174v1
|
2024-03-10T10:59:22Z
|
2024-03-10T10:59:22Z
|
Domain Adversarial Active Learning for Domain Generalization
Classification
|
Domain generalization models aim to learn cross-domain knowledge from source domain data, to improve performance on unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This paper argues that the impact of each sample on the model's generalization ability varies. Despite its small scale, a high-quality dataset can still attain a certain level of generalization ability. Motivated by this, we propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in domain generalization. First, we analyze that the objective of tasks is to maximize the inter-class distance within the same domain and minimize the intra-class distance across different domains. To achieve this objective, we design a domain adversarial selection method that prioritizes challenging samples. Second, we posit that even in a converged model, there are subsets of features that lack discriminatory power within each domain. We attempt to identify these feature subsets and optimize them by a constraint loss. We validate and analyze our DAAL algorithm on multiple domain generalization datasets, comparing it with various domain generalization algorithms and active learning algorithms. Our results demonstrate that the DAAL algorithm can achieve strong generalization ability with fewer data resources, thereby reducing data annotation costs in domain generalization tasks.
|
[
"['Jianting Chen' 'Ling Ding' 'Yunxiao Yang' 'Zaiyuan Di' 'Yang Xiang']"
] |
null | null |
2403.06183
| null | null |
http://arxiv.org/pdf/2403.06183v1
|
2024-03-10T11:50:34Z
|
2024-03-10T11:50:34Z
|
An Improved Analysis of Langevin Algorithms with Prior Diffusion for
Non-Log-Concave Sampling
|
Understanding the dimension dependency of computational complexity in high-dimensional sampling problem is a fundamental problem, both from a practical and theoretical perspective. Compared with samplers with unbiased stationary distribution, e.g., Metropolis-adjusted Langevin algorithm (MALA), biased samplers, e.g., Underdamped Langevin Dynamics (ULD), perform better in low-accuracy cases just because a lower dimension dependency in their complexities. Along this line, Freund et al. (2022) suggest that the modified Langevin algorithm with prior diffusion is able to converge dimension independently for strongly log-concave target distributions. Nonetheless, it remains open whether such property establishes for more general cases. In this paper, we investigate the prior diffusion technique for the target distributions satisfying log-Sobolev inequality (LSI), which covers a much broader class of distributions compared to the strongly log-concave ones. In particular, we prove that the modified Langevin algorithm can also obtain the dimension-independent convergence of KL divergence with different step size schedules. The core of our proof technique is a novel construction of an interpolating SDE, which significantly helps to conduct a more accurate characterization of the discrete updates of the overdamped Langevin dynamics. Our theoretical analysis demonstrates the benefits of prior diffusion for a broader class of target distributions and provides new insights into developing faster sampling algorithms.
|
[
"['Xunpeng Huang' 'Hanze Dong' 'Difan Zou' 'Tong Zhang']"
] |
null | null |
2403.06197
| null | null |
http://arxiv.org/pdf/2403.06197v1
|
2024-03-10T12:41:34Z
|
2024-03-10T12:41:34Z
|
DrFuse: Learning Disentangled Representation for Clinical Multi-Modal
Fusion with Missing Modality and Modal Inconsistency
|
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the significance of each data modality varies depending on the patient and the prediction target, resulting in inconsistent predictions and suboptimal model performance. To address these challenges, we propose DrFuse to achieve effective clinical multi-modal fusion. It tackles the missing modality issue by disentangling the features shared across modalities and those unique within each modality. Furthermore, we address the modal inconsistency issue via a disease-wise attention layer that produces the patient- and disease-wise weighting for each modality to make the final prediction. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR. Experimental results show that the proposed method significantly outperforms the state-of-the-art models. Our implementation is publicly available at https://github.com/dorothy-yao/drfuse.
|
[
"['Wenfang Yao' 'Kejing Yin' 'William K. Cheung' 'Jia Liu' 'Jing Qin']"
] |
null | null |
2403.06201
| null | null |
http://arxiv.org/pdf/2403.06201v1
|
2024-03-10T12:50:35Z
|
2024-03-10T12:50:35Z
|
Are You Being Tracked? Discover the Power of Zero-Shot Trajectory
Tracing with LLMs!
|
There is a burgeoning discussion around the capabilities of Large Language Models (LLMs) in acting as fundamental components that can be seamlessly incorporated into Artificial Intelligence of Things (AIoT) to interpret complex trajectories. This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition by employing a novel single-prompt technique that combines role-play and think step-by-step methodologies with unprocessed Inertial Measurement Unit (IMU) data. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios. In both test scenarios, LLMTrack not only meets but exceeds the performance benchmarks set by traditional machine learning approaches and even contemporary state-of-the-art deep learning models, all without the requirement of training on specialized datasets. The results of our research suggest that, with strategically designed prompts, LLMs can tap into their extensive knowledge base and are well-equipped to analyze raw sensor data with remarkable effectiveness.
|
[
"['Huanqi Yang' 'Sijie Ji' 'Rucheng Wu' 'Weitao Xu']"
] |
null | null |
2403.06230
| null | null |
http://arxiv.org/pdf/2403.06230v1
|
2024-03-10T15:01:50Z
|
2024-03-10T15:01:50Z
|
LinearAPT: An Adaptive Algorithm for the Fixed-Budget Thresholding
Linear Bandit Problem
|
In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined threshold under resource constraints. We present LinearAPT, a novel algorithm designed for the fixed budget setting of TLB, providing an efficient solution to optimize sequential decision-making. This algorithm not only offers a theoretical upper bound for estimated loss but also showcases robust performance on both synthetic and real-world datasets. Our contributions highlight the adaptability, simplicity, and computational efficiency of LinearAPT, making it a valuable addition to the toolkit for addressing complex sequential decision-making challenges.
|
[
"['Yun-Ang Wu' 'Yun-Da Tsai' 'Shou-De Lin']"
] |
null | null |
2403.06235
| null | null |
http://arxiv.org/pdf/2403.06235v1
|
2024-03-10T15:25:49Z
|
2024-03-10T15:25:49Z
|
Probabilistic Neural Circuits
|
Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressive enough to model complex probability distributions. Nevertheless, tractability comes at a cost: PCs are less expressive than neural networks. In this paper we introduce probabilistic neural circuits (PNCs), which strike a balance between PCs and neural nets in terms of tractability and expressive power. Theoretically, we show that PNCs can be interpreted as deep mixtures of Bayesian networks. Experimentally, we demonstrate that PNCs constitute powerful function approximators.
|
[
"['Pedro Zuidberg Dos Martires']"
] |
null | null |
2403.06239
| null | null |
http://arxiv.org/pdf/2403.06239v1
|
2024-03-10T15:38:20Z
|
2024-03-10T15:38:20Z
|
Cooperative Classification and Rationalization for Graph Generalization
|
Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://github.com/yuelinan/Codes-of-C2R.
|
[
"['Linan Yue' 'Qi Liu' 'Ye Liu' 'Weibo Gao' 'Fangzhou Yao' 'Wenfeng Li']"
] |
null | null |
2403.06251
| null | null |
http://arxiv.org/pdf/2403.06251v1
|
2024-03-10T16:34:21Z
|
2024-03-10T16:34:21Z
|
Online Multi-spectral Neuron Tracing
|
In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to update our enhanced discriminative correlation filter to conglutinate the tracing process. This distinctive offline-training-free schema differentiates us from other training-dependent tracing approaches like deep learning methods since no annotation is needed for our method. Besides, compared to other tracing methods requiring complicated set-up such as for clustering and graph multi-cut, our approach is much easier to be applied to new images. In fact, it only needs a starting bounding box of the tracing neuron, significantly reducing users' configuration effort. Our extensive experiments show that our training-free and easy-configured methodology allows fast and accurate neuron reconstructions in multi-spectral images.
|
[
"['Bin Duan' 'Yuzhang Shang' 'Dawen Cai' 'Yan Yan']"
] |
null | null |
2403.06259
| null | null |
http://arxiv.org/pdf/2403.06259v1
|
2024-03-10T16:57:10Z
|
2024-03-10T16:57:10Z
|
Editing Conceptual Knowledge for Large Language Models
|
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, while whether LLMs possess the capability to modify concepts remains unclear. This paper pioneers the investigation of editing conceptual knowledge for LLMs, by constructing a novel benchmark dataset ConceptEdit and establishing a suite of new metrics for evaluation. The experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge in LLMs, leading to poor performance. We anticipate this can inspire further progress in better understanding LLMs. Our project homepage is available at https://zjunlp.github.io/project/ConceptEdit.
|
[
"['Xiaohan Wang' 'Shengyu Mao' 'Ningyu Zhang' 'Shumin Deng' 'Yunzhi Yao'\n 'Yue Shen' 'Lei Liang' 'Jinjie Gu' 'Huajun Chen']"
] |
null | null |
2403.06265
| null | null |
http://arxiv.org/pdf/2403.06265v2
|
2024-06-22T16:00:49Z
|
2024-03-10T17:02:53Z
|
Unpacking Tokenization: Evaluating Text Compression and its Correlation
with Model Performance
|
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear. In this paper, we argue for the theoretical importance of compression, that can be viewed as 0-gram language modeling where equal probability is assigned to all tokens. We also demonstrate the empirical importance of compression for downstream success of pre-trained language models. We control the compression ability of several BPE tokenizers by varying the amount of documents available during their training: from 1 million documents to a character-based tokenizer equivalent to no training data at all. We then pre-train English language models based on those tokenizers and fine-tune them over several tasks. We show that there is a correlation between tokenizers' compression and models' downstream performance, suggesting that compression is a reliable intrinsic indicator of tokenization quality. These correlations are more pronounced for generation tasks (over classification) or for smaller models (over large ones). We replicated a representative part of our experiments on Turkish and found similar results, confirming that our results hold for languages with typological characteristics dissimilar to English. We conclude that building better compressing tokenizers is a fruitful avenue for further research and for improving overall model performance.
|
[
"['Omer Goldman' 'Avi Caciularu' 'Matan Eyal' 'Kris Cao' 'Idan Szpektor'\n 'Reut Tsarfaty']"
] |
null | null |
2403.06268
| null | null |
http://arxiv.org/pdf/2403.06268v1
|
2024-03-10T17:07:28Z
|
2024-03-10T17:07:28Z
|
Physics-Guided Abnormal Trajectory Gap Detection
|
Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object's possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this paper, we propose a Space Time-Aware Gap Detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a Dynamic Region Merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.
|
[
"['Arun Sharma' 'Shashi Shekhar']"
] |
null | null |
2403.06275
| null | null |
http://arxiv.org/pdf/2403.06275v1
|
2024-03-10T18:05:41Z
|
2024-03-10T18:05:41Z
|
UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation
|
Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves, with potential applications in tumor diagnosis and fat fraction estimation which are challenging to discern by conventional ultrasound B-mode images. Existing methods struggle with optimal window size selection and suffer from estimator instability, leading to degraded resolution images. To address this, here we propose a novel method called UNICORN (Ultrasound Nakagami Imaging via Score Matching and Adaptation), that offers an accurate, closed-form estimator for Nakagami parameter estimation in terms of the score function of ultrasonic envelope. Extensive experiments using simulation and real ultrasound RF data demonstrate UNICORN's superiority over conventional approaches in accuracy and resolution quality.
|
[
"['Kwanyoung Kim' 'Jaa-Yeon Lee' 'Jong Chul Ye']"
] |
null | null |
2403.06279
| null | null |
http://arxiv.org/pdf/2403.06279v2
|
2024-03-12T16:54:34Z
|
2024-03-10T18:13:22Z
|
Fine-tuning of diffusion models via stochastic control: entropy
regularization and beyond
|
This paper aims to develop and provide a rigorous treatment to the problem of entropy regularized fine-tuning in the context of continuous-time diffusion models, which was recently proposed by Uehara et al. (arXiv:2402.15194, 2024). The idea is to use stochastic control for sample generation, where the entropy regularizer is introduced to mitigate reward collapse. We also show how the analysis can be extended to fine-tuning involving a general $f$-divergence regularizer.
|
[
"['Wenpin Tang']"
] |
null | null |
2403.06289
| null | null |
http://arxiv.org/pdf/2403.06289v1
|
2024-03-10T19:05:12Z
|
2024-03-10T19:05:12Z
|
Understanding and Mitigating Human-Labelling Errors in Supervised
Contrastive Learning
|
Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-researched, their influence on Supervised Contrastive Learning (SCL) remains largely unexplored. In this paper, we show that human-labelling errors not only differ significantly from synthetic label errors, but also pose unique challenges in SCL, different to those in traditional supervised learning methods. Specifically, our results indicate they adversely impact the learning process in the ~99% of cases when they occur as false positive samples. Existing noise-mitigating methods primarily focus on synthetic label errors and tackle the unrealistic setting of very high synthetic noise rates (40-80%), but they often underperform on common image datasets due to overfitting. To address this issue, we introduce a novel SCL objective with robustness to human-labelling errors, SCL-RHE. SCL-RHE is designed to mitigate the effects of real-world mislabelled examples, typically characterized by much lower noise rates (<5%). We demonstrate that SCL-RHE consistently outperforms state-of-the-art representation learning and noise-mitigating methods across various vision benchmarks, by offering improved resilience against human-labelling errors.
|
[
"['Zijun Long' 'Lipeng Zhuang' 'George Killick' 'Richard McCreadie'\n 'Gerardo Aragon Camarasa' 'Paul Henderson']"
] |
null | null |
2403.06298
| null | null |
http://arxiv.org/pdf/2403.06298v1
|
2024-03-10T20:07:14Z
|
2024-03-10T20:07:14Z
|
Analysis of Total Variation Minimization for Clustered Federated
Learning
|
A key challenge in federated learning applications is the statistical heterogeneity of local datasets. Clustered federated learning addresses this challenge by identifying clusters of local datasets that are approximately homogeneous. One recent approach to clustered federated learning is generalized total variation minimization (GTVMin). This approach requires a similarity graph which can be obtained by domain expertise or in a data-driven fashion via graph learning techniques. Under a widely applicable clustering assumption, we derive an upper bound the deviation between GTVMin solutions and their cluster-wise averages. This bound provides valuable insights into the effectiveness and robustness of GTVMin in addressing statistical heterogeneity within federated learning environments.
|
[
"['A. Jung']"
] |
null | null |
2403.06302
| null | null |
http://arxiv.org/pdf/2403.06302v1
|
2024-03-10T20:22:06Z
|
2024-03-10T20:22:06Z
|
Nonparametric Automatic Differentiation Variational Inference with
Spline Approximation
|
Automatic Differentiation Variational Inference (ADVI) is efficient in learning probabilistic models. Classic ADVI relies on the parametric approach to approximate the posterior. In this paper, we develop a spline-based nonparametric approximation approach that enables flexible posterior approximation for distributions with complicated structures, such as skewness, multimodality, and bounded support. Compared with widely-used nonparametric variational inference methods, the proposed method is easy to implement and adaptive to various data structures. By adopting the spline approximation, we derive a lower bound of the importance weighted autoencoder and establish the asymptotic consistency. Experiments demonstrate the efficiency of the proposed method in approximating complex posterior distributions and improving the performance of generative models with incomplete data.
|
[
"['Yuda Shao' 'Shan Yu' 'Tianshu Feng']"
] |
null | null |
2403.06311
| null | null |
http://arxiv.org/pdf/2403.06311v1
|
2024-03-10T21:08:29Z
|
2024-03-10T21:08:29Z
|
How much data do you need? Part 2: Predicting DL class specific training
dataset sizes
|
This paper targets the question of predicting machine learning classification model performance, when taking into account the number of training examples per class and not just the overall number of training examples. This leads to the a combinatorial question, which combinations of number of training examples per class should be considered, given a fixed overall training dataset size. In order to solve this question, an algorithm is suggested which is motivated from special cases of space filling design of experiments. The resulting data are modeled using models like powerlaw curves and similar models, extended like generalized linear models i.e. by replacing the overall training dataset size by a parametrized linear combination of the number of training examples per label class. The proposed algorithm has been applied on the CIFAR10 and the EMNIST datasets.
|
[
"['Thomas Mühlenstädt' 'Jelena Frtunikj']"
] |
null | null |
2403.06313
| null | null |
http://arxiv.org/pdf/2403.06313v1
|
2024-03-10T21:18:54Z
|
2024-03-10T21:18:54Z
|
Optimal Policy Sparsification and Low Rank Decomposition for Deep
Reinforcement Learning
|
Deep reinforcement learning(DRL) has shown significant promise in a wide range of applications including computer games and robotics. Yet, training DRL policies consume extraordinary computing resources resulting in dense policies which are prone to overfitting. Moreover, inference with dense DRL policies limit their practical applications, especially in edge computing. Techniques such as pruning and singular value decomposition have been used with deep learning models to achieve sparsification and model compression to limit overfitting and reduce memory consumption. However, these techniques resulted in sub-optimal performance with notable decay in rewards. $L_1$ and $L_2$ regularization techniques have been proposed for neural network sparsification and sparse auto-encoder development, but their implementation in DRL environments has not been apparent. We propose a novel $L_0$-norm-regularization technique using an optimal sparsity map to sparsify DRL policies and promote their decomposition to a lower rank without decay in rewards. We evaluated our $L_0$-norm-regularization technique across five different environments (Cartpole-v1, Acrobat-v1, LunarLander-v2, SuperMarioBros-7.1.v0 and Surgical Robot Learning) using several on-policy and off-policy algorithms. We demonstrated that the $L_0$-norm-regularized DRL policy in the SuperMarioBros environment achieved 93% sparsity and gained 70% compression when subjected to low-rank decomposition, while significantly outperforming the dense policy. Additionally, the $L_0$-norm-regularized DRL policy in the Surgical Robot Learning environment achieved a 36% sparsification and gained 46% compression when decomposed to a lower rank, while being performant. The results suggest that our custom $L_0$-norm-regularization technique for sparsification of DRL policies is a promising avenue to reduce computational resources and limit overfitting.
|
[
"['Vikram Goddla']"
] |
null | null |
2403.06315
| null | null |
http://arxiv.org/pdf/2403.06315v1
|
2024-03-10T21:30:22Z
|
2024-03-10T21:30:22Z
|
A Study on Domain Generalization for Failure Detection through Human
Reactions in HRI
|
Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domain generalization in failure detection models trained on human facial expressions. Using two distinct datasets of humans reacting to videos where error occurs, one from a controlled lab setting and another collected online, we trained deep learning models on each dataset. When testing these models on the alternate dataset, we observed a significant performance drop. We reflect on the causes for the observed model behavior and leave recommendations. This work emphasizes the need for HRI research focusing on improving model robustness and real-life applicability.
|
[
"['Maria Teresa Parreira' 'Sukruth Gowdru Lingaraju'\n 'Adolfo Ramirez-Aristizabal' 'Manaswi Saha' 'Michael Kuniavsky'\n 'Wendy Ju']"
] |
null | null |
2403.06319
| null | null |
http://arxiv.org/pdf/2403.06319v1
|
2024-03-10T21:37:21Z
|
2024-03-10T21:37:21Z
|
Fake or Compromised? Making Sense of Malicious Clients in Federated
Learning
|
Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data. The field of FL security against poisoning attacks is plagued with confusion due to the proliferation of research that makes different assumptions about the capabilities of adversaries and the adversary models they operate under. Our work aims to clarify this confusion by presenting a comprehensive analysis of the various poisoning attacks and defensive aggregation rules (AGRs) proposed in the literature, and connecting them under a common framework. To connect existing adversary models, we present a hybrid adversary model, which lies in the middle of the spectrum of adversaries, where the adversary compromises a few clients, trains a generative (e.g., DDPM) model with their compromised samples, and generates new synthetic data to solve an optimization for a stronger (e.g., cheaper, more practical) attack against different robust aggregation rules. By presenting the spectrum of FL adversaries, we aim to provide practitioners and researchers with a clear understanding of the different types of threats they need to consider when designing FL systems, and identify areas where further research is needed.
|
[
"['Hamid Mozaffari' 'Sunav Choudhary' 'Amir Houmansadr']"
] |
null | null |
2403.06323
| null | null |
http://arxiv.org/pdf/2403.06323v1
|
2024-03-10T21:45:12Z
|
2024-03-10T21:45:12Z
|
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to
Standard RL
|
We study Risk-Sensitive Reinforcement Learning (RSRL) with the Optimized Certainty Equivalent (OCE) risk, which generalizes Conditional Value-at-risk (CVaR), entropic risk and Markowitz's mean-variance. Using an augmented Markov Decision Process (MDP), we propose two general meta-algorithms via reductions to standard RL: one based on optimistic algorithms and another based on policy optimization. Our optimistic meta-algorithm generalizes almost all prior RSRL theory with entropic risk or CVaR. Under discrete rewards, our optimistic theory also certifies the first RSRL regret bounds for MDPs with bounded coverability, e.g., exogenous block MDPs. Under discrete rewards, our policy optimization meta-algorithm enjoys both global convergence and local improvement guarantees in a novel metric that lower bounds the true OCE risk. Finally, we instantiate our framework with PPO, construct an MDP, and show that it learns the optimal risk-sensitive policy while prior algorithms provably fail.
|
[
"['Kaiwen Wang' 'Dawen Liang' 'Nathan Kallus' 'Wen Sun']"
] |
null | null |
2403.06326
| null | null |
http://arxiv.org/pdf/2403.06326v1
|
2024-03-10T22:14:54Z
|
2024-03-10T22:14:54Z
|
From Instructions to Constraints: Language Model Alignment with
Automatic Constraint Verification
|
User alignment is crucial for adapting general-purpose language models (LMs) to downstream tasks, but human annotations are often not available for all types of instructions, especially those with customized constraints. We observe that user instructions typically contain constraints. While assessing response quality in terms of the whole instruction is often costly, efficiently evaluating the satisfaction rate of constraints is feasible. We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints. Specifically, ACT uses constraint verifiers, which are typically easy to implement in practice, to compute constraint satisfaction rate (CSR) of each response. It samples multiple responses for each prompt and collect preference labels based on their CSR automatically. Subsequently, ACT adapts the LM to the target task through a ranking-based learning process. Experiments on fine-grained entity typing, abstractive summarization, and temporal question answering show that ACT is able to enhance LMs' capability to adhere to different classes of constraints, thereby improving task performance. Further experiments show that the constraint-following capabilities are transferable.
|
[
"['Fei Wang' 'Chao Shang' 'Sarthak Jain' 'Shuai Wang' 'Qiang Ning'\n 'Bonan Min' 'Vittorio Castelli' 'Yassine Benajiba' 'Dan Roth']"
] |
null | null |
2403.06328
| null | null |
http://arxiv.org/pdf/2403.06328v2
|
2024-05-28T23:42:28Z
|
2024-03-10T22:27:21Z
|
Transferable Reinforcement Learning via Generalized Occupancy Models
|
Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in principle enabling transfer to arbitrary reward functions through planning. However, autoregressive model rollouts suffer from compounding error, making model-based RL ineffective for long-horizon problems. Successor features offer an alternative by modeling a policy's long-term state occupancy, reducing policy evaluation under new tasks to linear reward regression. Yet, policy improvement with successor features can be challenging. This work proposes a novel class of models, i.e., generalized occupancy models (GOMs), that learn a distribution of successor features from a stationary dataset, along with a policy that acts to realize different successor features. These models can quickly select the optimal action for arbitrary new tasks. By directly modeling long-term outcomes in the dataset, GOMs avoid compounding error while enabling rapid transfer across reward functions. We present a practical instantiation of GOMs using diffusion models and show their efficacy as a new class of transferable models, both theoretically and empirically across various simulated robotics problems. Videos and code at https://weirdlabuw.github.io/gom/.
|
[
"['Chuning Zhu' 'Xinqi Wang' 'Tyler Han' 'Simon S. Du' 'Abhishek Gupta']"
] |
null | null |
2403.06338
| null | null |
http://arxiv.org/pdf/2403.06338v1
|
2024-03-10T23:11:05Z
|
2024-03-10T23:11:05Z
|
Disentangling shared and private latent factors in multimodal
Variational Autoencoders
|
Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality. Multimodal Variational Autoencoders, such as MVAE and MMVAE, are a natural choice for inferring those underlying latent factors and separating shared variation from private. In this work, we investigate their capability to reliably perform this disentanglement. In particular, we highlight a challenging problem setting where modality-specific variation dominates the shared signal. Taking a cross-modal prediction perspective, we demonstrate limitations of existing models, and propose a modification how to make them more robust to modality-specific variation. Our findings are supported by experiments on synthetic as well as various real-world multi-omics data sets.
|
[
"['Kaspar Märtens' 'Christopher Yau']"
] |
null | null |
2403.06342
| null | null |
http://arxiv.org/pdf/2403.06342v1
|
2024-03-10T23:44:55Z
|
2024-03-10T23:44:55Z
|
Separable Physics-informed Neural Networks for Solving the BGK Model of
the Boltzmann Equation
|
In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-free nature of PINNs offers significant advantages in handling high-dimensional partial differential equations (PDEs), challenges arise when applying quadrature rules for accurate integral evaluation in the BGK operator, which can compromise the mesh-free benefit and increase computational costs. To address this, we leverage the canonical polyadic decomposition structure of SPINNs and the linear nature of moment calculation, achieving a substantial reduction in computational expense for quadrature rule application. The multi-scale nature of the particle density function poses difficulties in precisely approximating macroscopic moments using neural networks. To improve SPINN training, we introduce the integration of Gaussian functions into SPINNs, coupled with a relative loss approach. This modification enables SPINNs to decay as rapidly as Maxwellian distributions, thereby enhancing the accuracy of macroscopic moment approximations. The relative loss design further ensures that both large and small-scale features are effectively captured by the SPINNs. The efficacy of our approach is demonstrated through a series of five numerical experiments, including the solution to a challenging 3D Riemann problem. These results highlight the potential of our novel method in efficiently and accurately addressing complex challenges in computational physics.
|
[
"['Jaemin Oh' 'Seung Yeon Cho' 'Seok-Bae Yun' 'Eunbyung Park'\n 'Youngjoon Hong']"
] |
null | null |
2403.06366
| null | null |
http://arxiv.org/pdf/2403.06366v2
|
2024-06-18T01:45:03Z
|
2024-03-11T01:36:37Z
|
Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach
|
Soft Q-learning is a variation of Q-learning designed to solve entropy regularized Markov decision problems where an agent aims to maximize the entropy regularized value function. Despite its empirical success, there have been limited theoretical studies of soft Q-learning to date. This paper aims to offer a novel and unified finite-time, control-theoretic analysis of soft Q-learning algorithms. We focus on two types of soft Q-learning algorithms: one utilizing the log-sum-exp operator and the other employing the Boltzmann operator. By using dynamical switching system models, we derive novel finite-time error bounds for both soft Q-learning algorithms. We hope that our analysis will deepen the current understanding of soft Q-learning by establishing connections with switching system models and may even pave the way for new frameworks in the finite-time analysis of other reinforcement learning algorithms.
|
[
"['Narim Jeong' 'Donghwan Lee']"
] |
null | null |
2403.06367
| null | null |
http://arxiv.org/pdf/2403.06367v1
|
2024-03-11T01:44:14Z
|
2024-03-11T01:44:14Z
|
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship
Tables
|
Feature augmentation from one-to-many relationship tables is a critical but challenging problem in ML model development. To augment good features, data scientists need to come up with SQL queries manually, which is time-consuming. Featuretools [1] is a widely used tool by the data science community to automatically augment the training data by extracting new features from relevant tables. It represents each feature as a group-by aggregation SQL query on relevant tables and can automatically generate these SQL queries. However, it does not include predicates in these queries, which significantly limits its application in many real-world scenarios. To overcome this limitation, we propose FEATAUG, a new feature augmentation framework that automatically extracts predicate-aware SQL queries from one-to-many relationship tables. This extension is not trivial because considering predicates will exponentially increase the number of candidate queries. As a result, the original Featuretools framework, which materializes all candidate queries, will not work and needs to be redesigned. We formally define the problem and model it as a hyperparameter optimization problem. We discuss how the Bayesian Optimization can be applied here and propose a novel warm-up strategy to optimize it. To make our algorithm more practical, we also study how to identify promising attribute combinations for predicates. We show that how the beam search idea can partially solve the problem and propose several techniques to further optimize it. Our experiments on four real-world datasets demonstrate that FeatAug extracts more effective features compared to Featuretools and other baselines. The code is open-sourced at https://github.com/sfu-db/FeatAug
|
[
"['Danrui Qi' 'Weiling Zheng' 'Jiannan Wang']"
] |
null | null |
2403.06382
| null | null |
http://arxiv.org/pdf/2403.06382v1
|
2024-03-11T02:24:32Z
|
2024-03-11T02:24:32Z
|
Pre-Trained Model Recommendation for Downstream Fine-tuning
|
As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task. Existing model selection techniques are often constrained in their scope and tend to overlook the nuanced relationships between models and tasks. In this paper, we present a pragmatic framework textbf{Fennec}, delving into a diverse, large-scale model repository while meticulously considering the intricate connections between tasks and models. The key insight is to map all models and historical tasks into a transfer-related subspace, where the distance between model vectors and task vectors represents the magnitude of transferability. A large vision model, as a proxy, infers a new task's representation in the transfer space, thereby circumventing the computational burden of extensive forward passes. We also investigate the impact of the inherent inductive bias of models on transfer results and propose a novel method called textbf{archi2vec} to encode the intricate structures of models. The transfer score is computed through straightforward vector arithmetic with a time complexity of $mathcal{O}(1)$. Finally, we make a substantial contribution to the field by releasing a comprehensive benchmark. We validate the effectiveness of our framework through rigorous testing on two benchmarks. The benchmark and the code will be publicly available in the near future.
|
[
"['Jiameng Bai' 'Sai Wu' 'Jie Song' 'Junbo Zhao' 'Gang Chen']"
] |
null | null |
2403.06388
| null | null |
http://arxiv.org/pdf/2403.06388v1
|
2024-03-11T02:47:21Z
|
2024-03-11T02:47:21Z
|
A Zero Trust Framework for Realization and Defense Against Generative AI
Attacks in Power Grid
|
Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and mitigation of such threats. First, a new zero trust system model of PGSC is designed and formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by realizing and defending against GenAI-driven cyber attacks. Second, in which a domain-specific generative adversarial networks (GAN)-based attack generation mechanism is developed to create a new vulnerability cyberspace for further understanding that threat. Third, tail-based risk realization metrics are developed and implemented for quantifying the extreme risk of a potential attack while leveraging a trust measurement approach for continuous validation. Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to detect the attacks that are generating synthetic identities with convincing user and distributed energy resources device profiles. Experimental results show the efficacy of the proposed zero trust framework that achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.
|
[
"['Md. Shirajum Munir' 'Sravanthi Proddatoori' 'Manjushree Muralidhara'\n 'Walid Saad' 'Zhu Han' 'Sachin Shetty']"
] |
null | null |
2403.06392
| null | null |
http://arxiv.org/pdf/2403.06392v1
|
2024-03-11T02:57:27Z
|
2024-03-11T02:57:27Z
|
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
|
Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks.
|
[
"['Yingtian Zou' 'Kenji Kawaguchi' 'Yingnan Liu' 'Jiashuo Liu'\n 'Mong-Li Lee' 'Wynne Hsu']"
] |
null | null |
2403.06397
| null | null |
http://arxiv.org/pdf/2403.06397v2
|
2024-03-12T02:13:51Z
|
2024-03-11T03:17:33Z
|
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe
Multi-Agent Reinforcement Learning
|
Safe Multi-agent reinforcement learning (safe MARL) has increasingly gained attention in recent years, emphasizing the need for agents to not only optimize the global return but also adhere to safety requirements through behavioral constraints. Some recent work has integrated control theory with multi-agent reinforcement learning to address the challenge of ensuring safety. However, there have been only very limited applications of Model Predictive Control (MPC) methods in this domain, primarily due to the complex and implicit dynamics characteristic of multi-agent environments. To bridge this gap, we propose a novel method called Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning (DeepSafeMPC). The key insight of DeepSafeMPC is leveraging a entralized deep learning model to well predict environmental dynamics. Our method applies MARL principles to search for optimal solutions. Through the employment of MPC, the actions of agents can be restricted within safe states concurrently. We demonstrate the effectiveness of our approach using the Safe Multi-agent MuJoCo environment, showcasing significant advancements in addressing safety concerns in MARL.
|
[
"['Xuefeng Wang' 'Henglin Pu' 'Hyung Jun Kim' 'Husheng Li']"
] |
null | null |
2403.06398
| null | null |
http://arxiv.org/pdf/2403.06398v3
|
2024-06-18T21:22:10Z
|
2024-03-11T03:19:45Z
|
On the Diminishing Returns of Width for Continual Learning
|
While deep neural networks have demonstrated groundbreaking performance in various settings, these models often suffer from emph{catastrophic forgetting} when trained on new tasks in sequence. Several works have empirically demonstrated that increasing the width of a neural network leads to a decrease in catastrophic forgetting but have yet to characterize the exact relationship between width and continual learning. We design one of the first frameworks to analyze Continual Learning Theory and prove that width is directly related to forgetting in Feed-Forward Networks (FFN). Specifically, we demonstrate that increasing network widths to reduce forgetting yields diminishing returns. We empirically verify our claims at widths hitherto unexplored in prior studies where the diminishing returns are clearly observed as predicted by our theory.
|
[
"['Etash Guha' 'Vihan Lakshman']"
] |
null | null |
2403.06402
| null | null |
http://arxiv.org/pdf/2403.06402v1
|
2024-03-11T03:28:13Z
|
2024-03-11T03:28:13Z
|
'One size doesn't fit all': Learning how many Examples to use for
In-Context Learning for Improved Text Classification
|
Predictive models in natural language processing (NLP) have evolved from training models from scratch to fine-tuning pre-trained models with labelled data. An extreme form of this fine-tuning involves in-context learning (ICL), where the output of a pre-trained generative model (frozen decoder parameters) is controlled only with variations in the input strings (called instructions or prompts). An important component of ICL is the use of a small number of labelled data instances as examples in the prompt. While existing work uses a static number of examples during inference for each data instance, in this paper we propose a novel methodology of dynamically adapting the number of examples as per the data. This is analogous to the use of a variable-sized neighborhood in k-nearest neighbors (k-NN) classifier. In our proposed workflow of adaptive ICL (AICL), the number of demonstrations to employ during the inference on a particular data instance is predicted by the Softmax posteriors of a classifier. The parameters of this classifier are fitted on the optimal number of examples in ICL required to correctly infer the label of each instance in the training set with the hypothesis that a test instance that is similar to a training instance should use the same (or a closely matching) number of few-shot examples. Our experiments show that our AICL method results in improvement in text classification task on several standard datasets.
|
[
"['Manish Chandra' 'Debasis Ganguly' 'Yiwen Li' 'Iadh Ounis']"
] |
null | null |
2403.06404
| null | null |
http://arxiv.org/abs/2403.06404v1
|
2024-03-11T03:31:35Z
|
2024-03-11T03:31:35Z
|
Cosine Scoring with Uncertainty for Neural Speaker Embedding
|
Uncertainty modeling in speaker representation aims to learn the variability present in speech utterances. While the conventional cosine-scoring is computationally efficient and prevalent in speaker recognition, it lacks the capability to handle uncertainty. To address this challenge, this paper proposes an approach for estimating uncertainty at the speaker embedding front-end and propagating it to the cosine scoring back-end. Experiments conducted on the VoxCeleb and SITW datasets confirmed the efficacy of the proposed method in handling uncertainty arising from embedding estimation. It achieved improvement with 8.5% and 9.8% average reductions in EER and minDCF compared to the conventional cosine similarity. It is also computationally efficient in practice.
|
[
"['Qiongqiong Wang' 'Kong Aik Lee']"
] |
null | null |
2403.06408
| null | null |
http://arxiv.org/pdf/2403.06408v1
|
2024-03-11T03:42:51Z
|
2024-03-11T03:42:51Z
|
What Makes Quantization for Large Language Models Hard? An Empirical
Study from the Lens of Perturbation
|
Quantization has emerged as a promising technique for improving the memory and computational efficiency of large language models (LLMs). Though the trade-off between performance and efficiency is well-known, there is still much to be learned about the relationship between quantization and LLM performance. To shed light on this relationship, we propose a new perspective on quantization, viewing it as perturbations added to the weights and activations of LLMs. We call this approach "the lens of perturbation". Using this lens, we conduct experiments with various artificial perturbations to explore their impact on LLM performance. Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization. To demonstrate the significance of our findings, we implement a simple non-uniform quantization approach based on our insights. Our experiments show that this approach achieves minimal performance degradation on both 4-bit weight quantization and 8-bit quantization for weights and activations. These results validate the correctness of our approach and highlight its potential to improve the efficiency of LLMs without sacrificing performance.
|
[
"['Zhuocheng Gong' 'Jiahao Liu' 'Jingang Wang' 'Xunliang Cai'\n 'Dongyan Zhao' 'Rui Yan']"
] |
null | null |
2403.06419
| null | null |
http://arxiv.org/pdf/2403.06419v1
|
2024-03-11T04:11:48Z
|
2024-03-11T04:11:48Z
|
Causal Multi-Label Feature Selection in Federated Setting
|
Multi-label feature selection serves as an effective mean for dealing with high-dimensional multi-label data. To achieve satisfactory performance, existing methods for multi-label feature selection often require the centralization of substantial data from multiple sources. However, in Federated setting, centralizing data from all sources and merging them into a single dataset is not feasible. To tackle this issue, in this paper, we study a challenging problem of causal multi-label feature selection in federated setting and propose a Federated Causal Multi-label Feature Selection (FedCMFS) algorithm with three novel subroutines. Specifically, FedCMFS first uses the FedCFL subroutine that considers the correlations among label-label, label-feature, and feature-feature to learn the relevant features (candidate parents and children) of each class label while preserving data privacy without centralizing data. Second, FedCMFS employs the FedCFR subroutine to selectively recover the missed true relevant features. Finally, FedCMFS utilizes the FedCFC subroutine to remove false relevant features. The extensive experiments on 8 datasets have shown that FedCMFS is effect for causal multi-label feature selection in federated setting.
|
[
"['Yukun Song' 'Dayuan Cao' 'Jiali Miao' 'Shuai Yang' 'Kui Yu']"
] |
null | null |
2403.06420
| null | null |
http://arxiv.org/pdf/2403.06420v2
|
2024-03-19T17:52:09Z
|
2024-03-11T04:13:26Z
|
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic
Manipulations With Large Language Models
|
Reinforcement learning (RL) has demonstrated its capability in solving various tasks but is notorious for its low sample efficiency. In this paper, we propose RLingua, a framework that can leverage the internal knowledge of large language models (LLMs) to reduce the sample complexity of RL in robotic manipulations. To this end, we first present a method for extracting the prior knowledge of LLMs by prompt engineering so that a preliminary rule-based robot controller for a specific task can be generated in a user-friendly manner. Despite being imperfect, the LLM-generated robot controller is utilized to produce action samples during rollouts with a decaying probability, thereby improving RL's sample efficiency. We employ TD3, the widely-used RL baseline method, and modify the actor loss to regularize the policy learning towards the LLM-generated controller. RLingua also provides a novel method of improving the imperfect LLM-generated robot controllers by RL. We demonstrate that RLingua can significantly reduce the sample complexity of TD3 in four robot tasks of panda_gym and achieve high success rates in 12 sampled sparsely rewarded robot tasks in RLBench, where the standard TD3 fails. Additionally, We validated RLingua's effectiveness in real-world robot experiments through Sim2Real, demonstrating that the learned policies are effectively transferable to real robot tasks. Further details about our work are available at our project website https://rlingua.github.io.
|
[
"['Liangliang Chen' 'Yutian Lei' 'Shiyu Jin' 'Ying Zhang' 'Liangjun Zhang']"
] |
null | null |
2403.06424
| null | null |
http://arxiv.org/pdf/2403.06424v1
|
2024-03-11T04:25:41Z
|
2024-03-11T04:25:41Z
|
Bridging Domains with Approximately Shared Features
|
Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strategy for feature selection. Existing literature is somewhat paradoxical: some advocate for learning invariant features from source domains, while others favor more diverse features. To address the challenge, we propose a statistical framework that distinguishes the utilities of features based on the variance of their correlation to label $y$ across domains. Under our framework, we design and analyze a learning procedure consisting of learning approximately shared feature representation from source tasks and fine-tuning it on the target task. Our theoretical analysis necessitates the importance of learning approximately shared features instead of only the strictly invariant features and yields an improved population risk compared to previous results on both source and target tasks, thus partly resolving the paradox mentioned above. Inspired by our theory, we proposed a more practical way to isolate the content (invariant+approximately shared) from environmental features and further consolidate our theoretical findings.
|
[
"['Ziliang Samuel Zhong' 'Xiang Pan' 'Qi Lei']"
] |
null | null |
2403.06425
| null | null |
http://arxiv.org/pdf/2403.06425v1
|
2024-03-11T04:26:18Z
|
2024-03-11T04:26:18Z
|
A Differential Geometric View and Explainability of GNN on Evolving
Graphs
|
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a trained GNN responds to graph evolution. We propose a smooth parameterization of the GNN predicted distributions using axiomatic attribution, where the distributions are on a low-dimensional manifold within a high-dimensional embedding space. We exploit the differential geometric viewpoint to model distributional evolution as smooth curves on the manifold. We reparameterize families of curves on the manifold and design a convex optimization problem to find a unique curve that concisely approximates the distributional evolution for human interpretation. Extensive experiments on node classification, link prediction, and graph classification tasks with evolving graphs demonstrate the better sparsity, faithfulness, and intuitiveness of the proposed method over the state-of-the-art methods.
|
[
"['Yazheng Liu' 'Xi Zhang' 'Sihong Xie']"
] |
null | null |
2403.06432
| null | null |
http://arxiv.org/pdf/2403.06432v1
|
2024-03-11T04:49:41Z
|
2024-03-11T04:49:41Z
|
Joint-Embedding Masked Autoencoder for Self-supervised Learning of
Dynamic Functional Connectivity from the Human Brain
|
Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. However, obtaining extensive labeled clinical data for training is often resource-intensive, making practical application difficult. Leveraging unlabeled data thus becomes crucial for representation learning in a label-scarce setting. Although generative self-supervised learning techniques, especially masked autoencoders, have shown promising results in representation learning in various domains, their application to dynamic graphs for dynamic functional connectivity remains underexplored, facing challenges in capturing high-level semantic representations. Here, we introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision. ST-JEMA employs a JEPA-inspired strategy for reconstructing dynamic graphs, which enables the learning of higher-level semantic representations considering temporal perspectives, addressing the challenges in fMRI data representation learning. Utilizing the large-scale UK Biobank dataset for self-supervised learning, ST-JEMA shows exceptional representation learning performance on dynamic functional connectivity demonstrating superiority over previous methods in predicting phenotypes and psychiatric diagnoses across eight benchmark fMRI datasets even with limited samples and effectiveness of temporal reconstruction on missing data scenarios. These findings highlight the potential of our approach as a robust representation learning method for leveraging label-scarce fMRI data.
|
[
"['Jungwon Choi' 'Hyungi Lee' 'Byung-Hoon Kim' 'Juho Lee']"
] |
null | null |
2403.06456
| null | null |
http://arxiv.org/pdf/2403.06456v1
|
2024-03-11T06:32:32Z
|
2024-03-11T06:32:32Z
|
A Survey of Learned Indexes for the Multi-dimensional Space
|
A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as "Learned Indexes." Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading to the development of "Learned Multi-dimensional Indexes". This survey focuses on learned multi-dimensional index structures. Specifically, it reviews the current state of this research area, explains the core concepts behind each proposed method, and classifies these methods based on several well-defined criteria. We present a taxonomy that classifies and categorizes each learned multi-dimensional index, and survey the existing literature on learned multi-dimensional indexes according to this taxonomy. Additionally, we present a timeline to illustrate the evolution of research on learned indexes. Finally, we highlight several open challenges and future research directions in this emerging and highly active field.
|
[
"['Abdullah Al-Mamun' 'Hao Wu' 'Qiyang He' 'Jianguo Wang' 'Walid G. Aref']"
] |
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