categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.08167
| null | null |
http://arxiv.org/pdf/2403.08167v2
|
2024-04-03T01:00:53Z
|
2024-03-13T01:38:42Z
|
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
|
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery. However, current pre-training frameworks are limited to two modalities, and designing a unified network to process different modalities (e.g., natural language, 2D molecular graphs, 3D molecular conformations, and 3D proteins) remains challenging due to inherent gaps among them. In this work, we propose MolBind, a framework that trains encoders for multiple modalities through contrastive learning, mapping all modalities to a shared feature space for multi-modal semantic alignment. To facilitate effective pre-training of MolBind on multiple modalities, we also build and collect a high-quality dataset with four modalities, MolBind-M4, including graph-language, conformation-language, graph-conformation, and conformation-protein paired data. MolBind shows superior zero-shot learning performance across a wide range of tasks, demonstrating its strong capability of capturing the underlying semantics of multiple modalities.
|
[
"['Teng Xiao' 'Chao Cui' 'Huaisheng Zhu' 'Vasant G. Honavar']"
] |
null | null |
2403.08171
| null | null |
http://arxiv.org/pdf/2403.08171v2
|
2024-07-03T00:26:58Z
|
2024-03-13T01:51:30Z
|
On Tractable $Φ$-Equilibria in Non-Concave Games
|
While Online Gradient Descent and other no-regret learning procedures are known to efficiently converge to a coarse correlated equilibrium in games where each agent's utility is concave in their own strategy, this is not the case when utilities are non-concave -- a common scenario in machine learning applications involving strategies parameterized by deep neural networks, or when agents' utilities are computed by neural networks, or both. Non-concave games introduce significant game-theoretic and optimization challenges: (i) Nash equilibria may not exist; (ii) local Nash equilibria, though existing, are intractable; and (iii) mixed Nash, correlated, and coarse correlated equilibria generally have infinite support and are intractable. To sidestep these challenges, we revisit the classical solution concept of $Phi$-equilibria introduced by Greenwald and Jafari [2003], which is guaranteed to exist for an arbitrary set of strategy modifications $Phi$ even in non-concave games [Stoltz and Lugosi, 2007]. However, the tractability of $Phi$-equilibria in such games remains elusive. In this paper, we initiate the study of tractable $Phi$-equilibria in non-concave games and examine several natural families of strategy modifications. We show that when $Phi$ is finite, there exists an efficient uncoupled learning algorithm that converges to the corresponding $Phi$-equilibria. Additionally, we explore cases where $Phi$ is infinite but consists of local modifications, showing that Online Gradient Descent can efficiently approximate $Phi$-equilibria in non-trivial regimes.
|
[
"['Yang Cai' 'Constantinos Daskalakis' 'Haipeng Luo' 'Chen-Yu Wei'\n 'Weiqiang Zheng']"
] |
null | null |
2403.08193
| null | null |
http://arxiv.org/pdf/2403.08193v1
|
2024-03-13T02:33:28Z
|
2024-03-13T02:33:28Z
|
Learning-driven Physically-aware Large-scale Circuit Gate Sizing
|
Gate sizing plays an important role in timing optimization after physical design. Existing machine learning-based gate sizing works cannot optimize timing on multiple timing paths simultaneously and neglect the physical constraint on layouts. They cause sub-optimal sizing solutions and low-efficiency issues when compared with commercial gate sizing tools. In this work, we propose a learning-driven physically-aware gate sizing framework to optimize timing performance on large-scale circuits efficiently. In our gradient descent optimization-based work, for obtaining accurate gradients, a multi-modal gate sizing-aware timing model is achieved via learning timing information on multiple timing paths and physical information on multiple-scaled layouts jointly. Then, gradient generation based on the sizing-oriented estimator and adaptive back-propagation are developed to update gate sizes. Our results demonstrate that our work achieves higher timing performance improvements in a faster way compared with the commercial gate sizing tool.
|
[
"['Yuyang Ye' 'Peng Xu' 'Lizheng Ren' 'Tinghuan Chen' 'Hao Yan' 'Bei Yu'\n 'Longxing Shi']"
] |
null | null |
2403.08194
| null | null |
http://arxiv.org/pdf/2403.08194v1
|
2024-03-13T02:33:57Z
|
2024-03-13T02:33:57Z
|
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify
Framework
|
Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many abstract latent representations may reconstruct observations, yet do they guarantee an adequate identification of the governing dynamics? This paper investigates this challenge from two angles: the use of physics inductive bias specific to the data being modeled, and a learn-to-identify strategy that separates forecasting objectives from the data used for the identification. We combine these two strategies in a novel framework for unsupervised meta-learning of hybrid latent dynamics (Meta-HyLaD) with: 1) a latent dynamic function that hybridize known mathematical expressions of prior physics with neural functions describing its unknown errors, and 2) a meta-learning formulation to learn to separately identify both components of the hybrid dynamics. Through extensive experiments on five physics and one biomedical systems, we provide strong evidence for the benefits of Meta-HyLaD to integrate rich prior knowledge while identifying their gap to observed data.
|
[
"['Yubo Ye' 'Sumeet Vadhavkar' 'Xiajun Jiang' 'Ryan Missel' 'Huafeng Liu'\n 'Linwei Wang']"
] |
null | null |
2403.08197
| null | null |
http://arxiv.org/pdf/2403.08197v1
|
2024-03-13T02:44:33Z
|
2024-03-13T02:44:33Z
|
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay
for Smart Healthcare
|
We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain the learned knowledge of previous domains. By replaying the synthetic data with the new real data during training, PAGE achieves a good balance between domain adaptation and knowledge retention. In addition, we incorporate an extended inductive conformal prediction (EICP) method into PAGE to produce a confidence score and a credibility value for each detection result. This makes the predictions interpretable and provides statistical guarantees for disease detection in smart healthcare applications. We demonstrate PAGE's effectiveness in domain-incremental disease detection with three distinct disease datasets collected from commercially available WMSs. PAGE achieves highly competitive performance against state-of-the-art with superior scalability, data privacy, and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical workload with the help of EICP.
|
[
"['Chia-Hao Li' 'Niraj K. Jha']"
] |
null | null |
2403.08199
| null | null |
http://arxiv.org/pdf/2403.08199v2
|
2024-03-16T01:02:35Z
|
2024-03-13T02:53:52Z
|
Deep Submodular Peripteral Networks
|
Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering graded pairwise preferences (GPC) is underexplored, despite a rich history in psychometrics. In this paper, we introduce deep submodular peripteral networks (DSPNs), a novel parametric family of submodular functions, and methods for their training using a contrastive-learning inspired GPC-ready strategy to connect and then tackle both of the above challenges. We introduce newly devised GPC-style "peripteral" loss which leverages numerically graded relationships between pairs of objects (sets in our case). Unlike traditional contrastive learning, our method utilizes graded comparisons, extracting more nuanced information than just binary-outcome comparisons, and contrasts sets of any size (not just two). We also define a novel suite of automatic sampling strategies for training, including active-learning inspired submodular feedback. We demonstrate DSPNs' efficacy in learning submodularity from a costly target submodular function showing superiority in downstream tasks such as experimental design and streaming applications.
|
[
"['Gantavya Bhatt' 'Arnav Das' 'Jeff Bilmes']"
] |
null | null |
2403.08203
| null | null |
http://arxiv.org/pdf/2403.08203v1
|
2024-03-13T02:55:27Z
|
2024-03-13T02:55:27Z
|
Learnable Community-Aware Transformer for Brain Connectome Analysis with
Token Clustering
|
Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological conditions, including Autism Spectrum Disorder (ASD) and biological differences, such as in gender. Traditional models have been constrained by the necessity of predefined community clusters, limiting their flexibility and adaptability in deciphering the brain's functional organization. Furthermore, these models were restricted by a fixed number of communities, hindering their ability to accurately represent the brain's dynamic nature. In this study, we present a token clustering brain transformer-based model ($texttt{TC-BrainTF}$) for joint community clustering and classification. Our approach proposes a novel token clustering (TC) module based on the transformer architecture, which utilizes learnable prompt tokens with orthogonal loss where each ROI embedding is projected onto the prompt embedding space, effectively clustering ROIs into communities and reducing the dimensions of the node representation via merging with communities. Our results demonstrate that our learnable community-aware model $texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets. Additionally, the qualitative analysis on $texttt{TC-BrainTF}$ has demonstrated the effectiveness of the designed TC module and its relevance to neuroscience interpretations.
|
[
"['Yanting Yang' 'Beidi Zhao' 'Zhuohao Ni' 'Yize Zhao' 'Xiaoxiao Li']"
] |
null | null |
2403.08204
| null | null |
http://arxiv.org/pdf/2403.08204v1
|
2024-03-13T02:56:31Z
|
2024-03-13T02:56:31Z
|
AutoDFP: Automatic Data-Free Pruning via Channel Similarity
Reconstruction
|
Structured pruning methods are developed to bridge the gap between the massive scale of neural networks and the limited hardware resources. Most current structured pruning methods rely on training datasets to fine-tune the compressed model, resulting in high computational burdens and being inapplicable for scenarios with stringent requirements on privacy and security. As an alternative, some data-free methods have been proposed, however, these methods often require handcraft parameter tuning and can only achieve inflexible reconstruction. In this paper, we propose the Automatic Data-Free Pruning (AutoDFP) method that achieves automatic pruning and reconstruction without fine-tuning. Our approach is based on the assumption that the loss of information can be partially compensated by retaining focused information from similar channels. Specifically, We formulate data-free pruning as an optimization problem, which can be effectively addressed through reinforcement learning. AutoDFP assesses the similarity of channels for each layer and provides this information to the reinforcement learning agent, guiding the pruning and reconstruction process of the network. We evaluate AutoDFP with multiple networks on multiple datasets, achieving impressive compression results. For instance, on the CIFAR-10 dataset, AutoDFP demonstrates a 2.87% reduction in accuracy loss compared to the recently proposed data-free pruning method DFPC with fewer FLOPs on VGG-16. Furthermore, on the ImageNet dataset, AutoDFP achieves 43.17% higher accuracy than the SOTA method with the same 80% preserved ratio on MobileNet-V1.
|
[
"['Siqi Li' 'Jun Chen' 'Jingyang Xiang' 'Chengrui Zhu' 'Yong Liu']"
] |
null | null |
2403.08207
| null | null |
http://arxiv.org/pdf/2403.08207v1
|
2024-03-13T03:03:40Z
|
2024-03-13T03:03:40Z
|
BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural
Network
|
Many computer vision and machine learning problems are modelled as learning tasks on heterogeneous graphs, featuring a wide array of relations from diverse types of nodes and edges. Heterogeneous graph neural networks (HGNNs) stand out as a promising neural model class designed for heterogeneous graphs. Built on traditional GNNs, existing HGNNs employ different parameter spaces to model the varied relationships. However, the practical effectiveness of existing HGNNs is often limited to simple heterogeneous graphs with few relation types. This paper first highlights and demonstrates that the standard approach employed by existing HGNNs inevitably leads to parameter explosion and relation collapse, making HGNNs less effective or impractical for complex heterogeneous graphs with numerous relation types. To overcome this issue, we introduce a novel framework, Blend&Grind-HGNN (BG-HGNN), which effectively tackles the challenges by carefully integrating different relations into a unified feature space manageable by a single set of parameters. This results in a refined HGNN method that is more efficient and effective in learning from heterogeneous graphs, especially when the number of relations grows. Our empirical studies illustrate that BG-HGNN significantly surpasses existing HGNNs in terms of parameter efficiency (up to 28.96 $times$), training throughput (up to 8.12 $times$), and accuracy (up to 1.07 $times$).
|
[
"['Junwei Su' 'Lingjun Mao' 'Chuan Wu']"
] |
null | null |
2403.08215
| null | null |
http://arxiv.org/pdf/2403.08215v1
|
2024-03-13T03:24:36Z
|
2024-03-13T03:24:36Z
|
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual
Semantic Segmentation for Autonomous Driving
|
Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are not available. Implicitly infusing the spatial geometric prior knowledge acquired by a duplex-encoder teacher model into a single-encoder student model is a practical, albeit less explored research avenue. This paper delves into this topic and resorts to knowledge distillation approaches to address this problem. We introduce the Learning to Infuse "X" (LIX) framework, with novel contributions in both logit distillation and feature distillation aspects. We present a mathematical proof that underscores the limitation of using a single fixed weight in decoupled knowledge distillation and introduce a logit-wise dynamic weight controller as a solution to this issue. Furthermore, we develop an adaptively-recalibrated feature distillation algorithm, including two technical novelties: feature recalibration via kernel regression and in-depth feature consistency quantification via centered kernel alignment. Extensive experiments conducted with intermediate-fusion and late-fusion networks across various public datasets provide both quantitative and qualitative evaluations, demonstrating the superior performance of our LIX framework when compared to other state-of-the-art approaches.
|
[
"['Sicen Guo' 'Zhiyuan Wu' 'Qijun Chen' 'Ioannis Pitas' 'Rui Fan']"
] |
null | null |
2403.08216
| null | null |
http://arxiv.org/pdf/2403.08216v2
|
2024-04-23T08:41:47Z
|
2024-03-13T03:28:39Z
|
PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise
|
Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues: 1) If the target distribution is manifold, due to the unmatch between the dimensions of the latent target distribution and the data distribution, flow-based models might perform badly. 2) Discrete data might make flow-based models collapse into a degenerate mixture of point masses. To sidestep such two issues, we propose PaddingFlow, a novel dequantization method, which improves normalizing flows with padding-dimensional noise. To implement PaddingFlow, only the dimension of normalizing flows needs to be modified. Thus, our method is easy to implement and computationally cheap. Moreover, the padding-dimensional noise is only added to the padding dimension, which means PaddingFlow can dequantize without changing data distributions. Implementing existing dequantization methods needs to change data distributions, which might degrade performance. We validate our method on the main benchmarks of unconditional density estimation, including five tabular datasets and four image datasets for Variational Autoencoder (VAE) models, and the Inverse Kinematics (IK) experiments which are conditional density estimation. The results show that PaddingFlow can perform better in all experiments in this paper, which means PaddingFlow is widely suitable for various tasks. The code is available at: https://github.com/AdamQLMeng/PaddingFlow.
|
[
"['Qinglong Meng' 'Chongkun Xia' 'Xueqian Wang']"
] |
null | null |
2403.08217
| null | null |
http://arxiv.org/pdf/2403.08217v1
|
2024-03-13T03:31:26Z
|
2024-03-13T03:31:26Z
|
Research on the Application of Deep Learning-based BERT Model in
Sentiment Analysis
|
This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed explanation, it elucidates the application effects and optimization strategies of BERT models in sentiment analysis, supported by experimental validation. The experimental findings indicate that BERT models exhibit robust performance in sentiment analysis tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes by summarizing the potential applications of BERT models in sentiment analysis and suggests directions for future research and practical implementations.
|
[
"['Yichao Wu' 'Zhengyu Jin' 'Chenxi Shi' 'Penghao Liang' 'Tong Zhan']"
] |
null | null |
2403.08220
| null | null |
http://arxiv.org/pdf/2403.08220v2
|
2024-05-20T07:20:36Z
|
2024-03-13T03:45:14Z
|
Derivative-informed neural operator acceleration of geometric MCMC for
infinite-dimensional Bayesian inverse problems
|
We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse problems (BIPs). While geometric MCMC employs high-quality proposals that adapt to posterior local geometry, it requires repeated computations of gradients and Hessians of the log-likelihood, which becomes prohibitive when the parameter-to-observable (PtO) map is defined through expensive-to-solve parametric partial differential equations (PDEs). We consider a delayed-acceptance geometric MCMC method driven by a neural operator surrogate of the PtO map, where the proposal exploits fast surrogate predictions of the log-likelihood and, simultaneously, its gradient and Hessian. To achieve a substantial speedup, the surrogate must accurately approximate the PtO map and its Jacobian, which often demands a prohibitively large number of PtO map samples via conventional operator learning methods. In this work, we present an extension of derivative-informed operator learning [O'Leary-Roseberry et al., J. Comput. Phys., 496 (2024)] that uses joint samples of the PtO map and its Jacobian. This leads to derivative-informed neural operator (DINO) surrogates that accurately predict the observables and posterior local geometry at a significantly lower training cost than conventional methods. Cost and error analysis for reduced basis DINO surrogates are provided. Numerical studies demonstrate that DINO-driven MCMC generates effective posterior samples 3--9 times faster than geometric MCMC and 60--97 times faster than prior geometry-based MCMC. Furthermore, the training cost of DINO surrogates breaks even compared to geometric MCMC after just 10--25 effective posterior samples.
|
[
"['Lianghao Cao' \"Thomas O'Leary-Roseberry\" 'Omar Ghattas']"
] |
null | null |
2403.08222
| null | null |
http://arxiv.org/pdf/2403.08222v1
|
2024-03-13T03:47:08Z
|
2024-03-13T03:47:08Z
|
Robust Decision Aggregation with Adversarial Experts
|
We consider a binary decision aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully with proper incentive, while the adversarial experts can report arbitrarily. The decision maker needs to design a robust aggregator to forecast the true state of the world based on the reports of experts. The decision maker does not know the specific information structure, which is a joint distribution of signals, states, and strategies of adversarial experts. We want to find the optimal aggregator minimizing regret under the worst information structure. The regret is defined by the difference in expected loss between the aggregator and a benchmark who makes the optimal decision given the joint distribution and reports of truthful experts. We prove that when the truthful experts are symmetric and adversarial experts are not too numerous, the truncated mean is optimal, which means that we remove some lowest reports and highest reports and take averaging among the left reports. Moreover, for many settings, the optimal aggregators are in the family of piecewise linear functions. The regret is independent of the total number of experts but only depends on the ratio of adversaries. We evaluate our aggregators by numerical experiment in an ensemble learning task. We also obtain some negative results for the aggregation problem with adversarial experts under some more general information structures and experts' report space.
|
[
"['Yongkang Guo' 'Yuqing Kong']"
] |
null | null |
2403.08239
| null | null |
http://arxiv.org/abs/2403.08239v1
|
2024-03-13T04:45:40Z
|
2024-03-13T04:45:40Z
|
Continuous Object State Recognition for Cooking Robots Using Pre-Trained
Vision-Language Models and Black-box Optimization
|
The state recognition of the environment and objects by robots is generally based on the judgement of the current state as a classification problem. On the other hand, state changes of food in cooking happen continuously and need to be captured not only at a certain time point but also continuously over time. In addition, the state changes of food are complex and cannot be easily described by manual programming. Therefore, we propose a method to recognize the continuous state changes of food for cooking robots through the spoken language using pre-trained large-scale vision-language models. By using models that can compute the similarity between images and texts continuously over time, we can capture the state changes of food while cooking. We also show that by adjusting the weighting of each text prompt based on fitting the similarity changes to a sigmoid function and then performing black-box optimization, more accurate and robust continuous state recognition can be achieved. We demonstrate the effectiveness and limitations of this method by performing the recognition of water boiling, butter melting, egg cooking, and onion stir-frying.
|
[
"['Kento Kawaharazuka' 'Naoaki Kanazawa' 'Yoshiki Obinata' 'Kei Okada'\n 'Masayuki Inaba']"
] |
null | null |
2403.08245
| null | null |
http://arxiv.org/pdf/2403.08245v1
|
2024-03-13T05:00:23Z
|
2024-03-13T05:00:23Z
|
Scattered Mixture-of-Experts Implementation
|
We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and overcoming some of the limitations to improve inference and training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We introduce ParallelLinear, the main component we use to build our implementation and the various kernels used to speed up the operation. We benchmark our implementation against Megablocks, and show that it enables a higher throughput and lower memory footprint. We also show how ParallelLinear enables extension of the Mixture-of-Experts concept by demonstrating with an implementation of Mixture of Attention.
|
[
"['Shawn Tan' 'Yikang Shen' 'Rameswar Panda' 'Aaron Courville']"
] |
null | null |
2403.08246
| null | null |
http://arxiv.org/pdf/2403.08246v1
|
2024-03-13T05:00:42Z
|
2024-03-13T05:00:42Z
|
Towards Unified Modeling for Positive and Negative Preferences in
Sign-Aware Recommendation
|
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different semantics of negative and positive links, existing works utilize two independent encoders to model users' positive and negative preferences, respectively. However, these approaches cannot learn the negative preferences from high-order heterogeneous interactions between users and items formed by multiple links with different signs, resulting in inaccurate and incomplete negative user preferences. To cope with these intractable issues, we propose a novel textbf{L}ight textbf{S}igned textbf{G}raph Convolution Network specifically for textbf{Rec}ommendation (textbf{LSGRec}), which adopts a unified modeling approach to simultaneously model high-order users' positive and negative preferences on a signed user-item interaction graph. Specifically, for the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links, while high-order negative preferences are propagated along positive edges. Then, recommendation results are generated based on positive preferences and optimized with negative ones. Finally, we train representations of users and items through different auxiliary tasks. Extensive experiments on three real-world datasets demonstrate that our method outperforms existing baselines regarding performance and computational efficiency. Our code is available at url{https://anonymous.4open.science/r/LSGRec-BB95}.
|
[
"['Yuting Liu' 'Yizhou Dang' 'Yuliang Liang' 'Qiang Liu' 'Guibing Guo'\n 'Jianzhe Zhao' 'Xingwei Wang']"
] |
null | null |
2403.08254
| null | null |
http://arxiv.org/pdf/2403.08254v1
|
2024-03-13T05:11:24Z
|
2024-03-13T05:11:24Z
|
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and
Prospects
|
Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars.
|
[
"['Na Li' 'Chunyi Zhou' 'Yansong Gao' 'Hui Chen' 'Anmin Fu' 'Zhi Zhang'\n 'Yu Shui']"
] |
null | null |
2403.08258
| null | null |
http://arxiv.org/pdf/2403.08258v2
|
2024-05-21T02:06:26Z
|
2024-03-13T05:20:45Z
|
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition
|
Conformer-based attention models have become the de facto backbone model for Automatic Speech Recognition tasks. A blank symbol is usually introduced to align the input and output sequences for CTC or RNN-T models. Unfortunately, the long input length overloads computational budget and memory consumption quadratically by attention mechanism. In this work, we propose a "Skip-and-Recover" Conformer architecture, named Skipformer, to squeeze sequence input length dynamically and inhomogeneously. Skipformer uses an intermediate CTC output as criteria to split frames into three groups: crucial, skipping and ignoring. The crucial group feeds into next conformer blocks and its output joint with skipping group by original temporal order as the final encoder output. Experiments show that our model reduces the input sequence length by 31 times on Aishell-1 and 22 times on Librispeech corpus. Meanwhile, the model can achieve better recognition accuracy and faster inference speed than recent baseline models. Our code is open-sourced and available online.
|
[
"['Wenjing Zhu' 'Sining Sun' 'Changhao Shan' 'Peng Fan' 'Qing Yang']"
] |
null | null |
2403.08265
| null | null |
http://arxiv.org/pdf/2403.08265v2
|
2024-03-14T05:18:08Z
|
2024-03-13T05:32:13Z
|
Random Search as a Baseline for Sparse Neural Network Architecture
Search
|
Sparse neural networks have shown similar or better generalization performance than their dense counterparts while having higher parameter efficiency. This has motivated a number of works to learn or search for high performing sparse networks. While reports of task performance or efficiency gains are impressive, standard baselines are lacking leading to poor comparability and unreliable reproducibility across methods. In this work, we propose Random Search as a baseline algorithm for finding good sparse configurations and study its performance. We apply Random Search on the node space of an overparameterized network with the goal of finding better initialized sparse sub-networks that are positioned more advantageously in the loss landscape. We record the post-training performances of the found sparse networks and at various levels of sparsity, and compare against both their fully connected parent networks and random sparse configurations at the same sparsity levels. First, we demonstrate performance at different levels of sparsity and highlight that a significant level of performance can still be preserved even when the network is highly sparse. Second, we observe that for this sparse architecture search task, initialized sparse networks found by Random Search neither perform better nor converge more efficiently than their random counterparts. Thus we conclude that Random Search may be viewed as a reasonable neutral baseline for sparsity search methods.
|
[
"['Rezsa Farahani']"
] |
null | null |
2403.08267
| null | null |
http://arxiv.org/pdf/2403.08267v1
|
2024-03-13T05:35:55Z
|
2024-03-13T05:35:55Z
|
SNOW-SCA: ML-assisted Side-Channel Attack on SNOW-V
|
This paper presents SNOW-SCA, the first power side-channel analysis (SCA) attack of a 5G mobile communication security standard candidate, SNOW-V, running on a 32-bit ARM Cortex-M4 microcontroller. First, we perform a generic known-key correlation (KKC) analysis to identify the leakage points. Next, a correlation power analysis (CPA) attack is performed, which reduces the attack complexity to two key guesses for each key byte. The correct secret key is then uniquely identified utilizing linear discriminant analysis (LDA). The profiled SCA attack with LDA achieves 100% accuracy after training with $<200$ traces, which means the attack succeeds with just a single trace. Overall, using the textit{combined CPA and LDA attack} model, the correct secret key byte is recovered with <50 traces collected using the ChipWhisperer platform. The entire 256-bit secret key of SNOW-V can be recovered incrementally using the proposed SCA attack. Finally, we suggest low-overhead countermeasures that can be used to prevent these SCA attacks.
|
[
"['Harshit Saurabh' 'Anupam Golder' 'Samarth Shivakumar Titti'\n 'Suparna Kundu' 'Chaoyun Li' 'Angshuman Karmakar' 'Debayan Das']"
] |
null | null |
2403.08291
| null | null |
http://arxiv.org/pdf/2403.08291v2
|
2024-04-25T03:47:13Z
|
2024-03-13T06:54:15Z
|
CleanAgent: Automating Data Standardization with LLM-based Agents
|
Data standardization is a crucial part in data science life cycle. While tools like Pandas offer robust functionalities, their complexity and the manual effort required for customizing code to diverse column types pose significant challenges. Although large language models (LLMs) like ChatGPT have shown promise in automating this process through natural language understanding and code generation, it still demands expert-level programming knowledge and continuous interaction for prompt refinement. To solve these challenges, our key idea is to propose a Python library with declarative, unified APIs for standardizing column types, simplifying the code generation of LLM with concise API calls. We first propose Dataprep.Clean which is written as a component of the Dataprep Library, offers a significant reduction in complexity by enabling the standardization of specific column types with a single line of code. Then we introduce the CleanAgent framework integrating Dataprep.Clean and LLM-based agents to automate the data standardization process. With CleanAgent, data scientists need only provide their requirements once, allowing for a hands-free, automatic standardization process.
|
[
"['Danrui Qi' 'Jiannan Wang']"
] |
null | null |
2403.08309
| null | null |
http://arxiv.org/pdf/2403.08309v2
|
2024-03-14T04:24:41Z
|
2024-03-13T07:38:20Z
|
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain
Reinforcement Learning From AI Feedback
|
Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate. Analysis suggests that the decrease in satisfaction rate is mainly due to some responses becoming less helpful, particularly in terms of correctness and truthfulness, highlighting practical limitations of basic RLAIF. In this paper, we propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF). This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. Additionally, it employs AI for Red Teaming, further improving the model's harmlessness. Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses. Compared to the policy model before Reinforcement Learning (RL), it achieves an increase of 2.08% in satisfaction rate, effectively addressing the issue of a decrease of 4.58% in satisfaction rate after basic RLAIF.
|
[
"['Ang Li' 'Qiugen Xiao' 'Peng Cao' 'Jian Tang' 'Yi Yuan' 'Zijie Zhao'\n 'Xiaoyuan Chen' 'Liang Zhang' 'Xiangyang Li' 'Kaitong Yang' 'Weidong Guo'\n 'Yukang Gan' 'Xu Yu' 'Daniell Wang' 'Ying Shan']"
] |
null | null |
2403.08319
| null | null |
http://arxiv.org/pdf/2403.08319v2
|
2024-06-22T08:31:40Z
|
2024-03-13T08:02:23Z
|
Knowledge Conflicts for LLMs: A Survey
|
This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. By categorizing these conflicts, exploring the causes, examining the behaviors of LLMs under such conflicts, and reviewing available solutions, this survey aims to shed light on strategies for improving the robustness of LLMs, thereby serving as a valuable resource for advancing research in this evolving area.
|
[
"['Rongwu Xu' 'Zehan Qi' 'Zhijiang Guo' 'Cunxiang Wang' 'Hongru Wang'\n 'Yue Zhang' 'Wei Xu']"
] |
null | null |
2403.08331
| null | null |
http://arxiv.org/abs/2403.08331v1
|
2024-03-13T08:34:40Z
|
2024-03-13T08:34:40Z
|
Bayesian Optimization that Limits Search Region to Lower Dimensions
Utilizing Local GPR
|
Optimization of product and system characteristics is required in many fields, including design and control. Bayesian optimization (BO) is often used when there are high observing costs, because BO theoretically guarantees an upper bound on regret. However, computational costs increase exponentially with the number of parameters to be optimized, decreasing search efficiency. We propose a BO that limits the search region to lower dimensions and utilizes local Gaussian process regression (LGPR) to scale the BO to higher dimensions. LGPR treats the low-dimensional search region as "local," improving prediction accuracies there. The LGPR model is trained on a local subset of data specific to that region. This improves prediction accuracy and search efficiency and reduces the time complexity of matrix inversion in the Gaussian process regression. In evaluations with 20D Ackley and Rosenbrock functions, search efficiencies are equal to or higher than those of the compared methods, improved by about 69% and 40% from the case without LGPR. We apply our method to an automatic design task for a power semiconductor device. We successfully reduce the specific on-resistance to 25% better than a conventional method and 3.4% better than without LGPR.
|
[
"['Yasunori Taguchi' 'Hiro Gangi']"
] |
null | null |
2403.08333
| null | null |
http://arxiv.org/abs/2403.08333v3
|
2024-05-31T22:36:34Z
|
2024-03-13T08:37:31Z
|
Fast Inference of Removal-Based Node Influence
|
Graph neural networks (GNNs) are widely utilized to capture the information spreading patterns in graphs. While remarkable performance has been achieved, there is a new trending topic of evaluating node influence. We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node. A real-world application is, "In the task of predicting Twitter accounts' polarity, had a particular account been removed, how would others' polarity change?". We use the GNN as a surrogate model whose prediction could simulate the change of nodes or edges caused by node removal. Our target is to obtain the influence score for every node, and a straightforward way is to alternately remove every node and apply the trained GNN on the modified graph to generate new predictions. It is reliable but time-consuming, so we need an efficient method. The related lines of work, such as graph adversarial attack and counterfactual explanation, cannot directly satisfy our needs, since their problem settings are different. We propose an efficient, intuitive, and effective method, NOde-Removal-based fAst GNN inference (NORA), which uses the gradient information to approximate the node-removal influence. It only costs one forward propagation and one backpropagation to approximate the influence score for all nodes. Extensive experiments on six datasets and six GNN models verify the effectiveness of NORA. Our code is available at https://github.com/weikai-li/NORA.git.
|
[
"['Weikai Li' 'Zhiping Xiao' 'Xiao Luo' 'Yizhou Sun']"
] |
null | null |
2403.08335
| null | null |
http://arxiv.org/pdf/2403.08335v2
|
2024-06-15T13:06:08Z
|
2024-03-13T08:40:49Z
|
A Sparsity Principle for Partially Observable Causal Representation
Learning
|
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations. We instead consider a partially observed setting, in which each measurement only provides information about a subset of the underlying causal state. Prior work has studied this setting with multiple domains or views, each depending on a fixed subset of latents. Here, we focus on learning from unpaired observations from a dataset with an instance-dependent partial observability pattern. Our main contribution is to establish two identifiability results for this setting: one for linear mixing functions without parametric assumptions on the underlying causal model, and one for piecewise linear mixing functions with Gaussian latent causal variables. Based on these insights, we propose two methods for estimating the underlying causal variables by enforcing sparsity in the inferred representation. Experiments on different simulated datasets and established benchmarks highlight the effectiveness of our approach in recovering the ground-truth latents.
|
[
"['Danru Xu' 'Dingling Yao' 'Sébastien Lachapelle' 'Perouz Taslakian'\n 'Julius von Kügelgen' 'Francesco Locatello' 'Sara Magliacane']"
] |
null | null |
2403.08337
| null | null |
http://arxiv.org/pdf/2403.08337v2
|
2024-06-12T14:53:58Z
|
2024-03-13T08:41:55Z
|
LLM-Assisted Light: Leveraging Large Language Model Capabilities for
Human-Mimetic Traffic Signal Control in Complex Urban Environments
|
Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learning (RL), frequently exhibit deficiencies in managing the complexities and variabilities of urban traffic flows, constrained by their limited capacity for adaptation to unfamiliar scenarios. In response to these limitations, this work introduces an innovative approach that integrates Large Language Models (LLMs) into TSC, harnessing their advanced reasoning and decision-making faculties. Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information. This design places the LLM at the center of the decision-making process, combining external traffic data with established TSC methods. Moreover, a simulation platform is developed to corroborate the efficacy of the proposed framework. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments without the need for additional training. Notably, in cases of Sensor Outage (SO), our approach surpasses conventional RL-based systems by reducing the average waiting time by $20.4%$. This research signifies a notable advance in TSC strategies and paves the way for the integration of LLMs into real-world, dynamic scenarios, highlighting their potential to revolutionize traffic management. The related code is available at https://github.com/Traffic-Alpha/LLM-Assisted-Light.
|
[
"['Maonan Wang' 'Aoyu Pang' 'Yuheng Kan' 'Man-On Pun' 'Chung Shue Chen'\n 'Bo Huang']"
] |
null | null |
2403.08344
| null | null |
http://arxiv.org/pdf/2403.08344v1
|
2024-03-13T08:49:40Z
|
2024-03-13T08:49:40Z
|
STMPL: Human Soft-Tissue Simulation
|
In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simulation of realistic interactions. Our method builds upon the SMPL model, which generates human body shapes considering rigid transformations. We extend SMPL by incorporating a soft tissue layer and an intuitive representation of external forces applied to the body during object interactions. Specifically, we mapped the 3D body shape and soft tissue and applied external forces to 2D UV maps. Leveraging a UNET architecture designed for 2D data, our approach achieves high-accuracy inference in real time. Our experiment shows that our method achieves plausible deformation of the soft tissue layer, even for unseen scenarios.
|
[
"['Anton Agafonov' 'Lihi Zelnik-Manor']"
] |
null | null |
2403.08352
| null | null |
http://arxiv.org/pdf/2403.08352v1
|
2024-03-13T09:00:38Z
|
2024-03-13T09:00:38Z
|
Data augmentation with automated machine learning: approaches and
performance comparison with classical data augmentation methods
|
Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with desired properties. Despite its effectiveness, the process is often challenging because of the time-consuming trial and error procedures for creating and testing different candidate augmentations and their hyperparameters manually. Automated data augmentation methods aim to automate the process. State-of-the-art approaches typically rely on automated machine learning (AutoML) principles. This work presents a comprehensive survey of AutoML-based data augmentation techniques. We discuss various approaches for accomplishing data augmentation with AutoML, including data manipulation, data integration and data synthesis techniques. We present extensive discussion of techniques for realizing each of the major subtasks of the data augmentation process: search space design, hyperparameter optimization and model evaluation. Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches. The results show that AutoML methods for data augmentation currently outperform state-of-the-art techniques based on conventional approaches.
|
[
"['Alhassan Mumuni' 'Fuseini Mumuni']"
] |
null | null |
2403.08362
| null | null |
http://arxiv.org/pdf/2403.08362v2
|
2024-05-27T13:50:55Z
|
2024-03-13T09:22:30Z
|
Mean-Field Microcanonical Gradient Descent
|
Microcanonical gradient descent is a sampling procedure for energy-based models allowing for efficient sampling of distributions in high dimension. It works by transporting samples from a high-entropy distribution, such as Gaussian white noise, to a low-energy region using gradient descent. We put this model in the framework of normalizing flows, showing how it can often overfit by losing an unnecessary amount of entropy in the descent. As a remedy, we propose a mean-field microcanonical gradient descent that samples several weakly coupled data points simultaneously, allowing for better control of the entropy loss while paying little in terms of likelihood fit. We study these models in the context of financial time series, illustrating the improvements on both synthetic and real data.
|
[
"['Marcus Häggbom' 'Morten Karlsmark' 'Joakim Andén']"
] |
null | null |
2403.08364
| null | null |
http://arxiv.org/pdf/2403.08364v1
|
2024-03-13T09:24:59Z
|
2024-03-13T09:24:59Z
|
Decoupled Federated Learning on Long-Tailed and Non-IID data with
Feature Statistics
|
Federated learning is designed to enhance data security and privacy, but faces challenges when dealing with heterogeneous data in long-tailed and non-IID distributions. This paper explores an overlooked scenario where tail classes are sparsely distributed over a few clients, causing the models trained with these classes to have a lower probability of being selected during client aggregation, leading to slower convergence rates and poorer model performance. To address this issue, we propose a two-stage Decoupled Federated learning framework using Feature Statistics (DFL-FS). In the first stage, the server estimates the client's class coverage distributions through masked local feature statistics clustering to select models for aggregation to accelerate convergence and enhance feature learning without privacy leakage. In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics and utilizes resampling and weighted covariance to calibrate the global classifier to enhance the model's adaptability to long-tailed data distributions. We conducted experiments on CIFAR10-LT and CIFAR100-LT datasets with various long-tailed rates. The results demonstrate that our method outperforms state-of-the-art methods in both accuracy and convergence rate.
|
[
"['Zhuoxin Chen' 'Zhenyu Wu' 'Yang Ji']"
] |
null | null |
2403.08370
| null | null |
http://arxiv.org/pdf/2403.08370v3
|
2024-07-13T11:01:14Z
|
2024-03-13T09:31:50Z
|
SMART: Submodular Data Mixture Strategy for Instruction Tuning
|
Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task proportions during finetuning, but finding the right balance remains challenging. Unfortunately, there's currently no systematic method beyond manual tuning or relying on practitioners' intuition. In this paper, we introduce SMART (Submodular data Mixture strAtegy for instRuction Tuning) - a novel data mixture strategy which makes use of a submodular function to assign importance scores to tasks which are then used to determine the mixture weights. Given a fine-tuning budget, SMART redistributes the budget among tasks and selects non-redundant samples from each task. Experimental results demonstrate that SMART significantly outperforms traditional methods such as examples proportional mixing and equal mixing. Furthermore, SMART facilitates the creation of data mixtures based on a few representative subsets of tasks alone and through task pruning analysis, we reveal that in a limited budget setting, allocating budget among a subset of representative tasks yields superior performance compared to distributing the budget among all tasks. The code for reproducing our results is open-sourced at https://github.com/kowndinya-renduchintala/SMART.
|
[
"['H S V N S Kowndinya Renduchintala' 'Sumit Bhatia' 'Ganesh Ramakrishnan']"
] |
null | null |
2403.08376
| null | null |
http://arxiv.org/pdf/2403.08376v1
|
2024-03-13T09:39:15Z
|
2024-03-13T09:39:15Z
|
Nonlinear Manifold Learning Determines Microgel Size from Raman
Spectroscopy
|
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross-linked polymer) samples in a diameter range of 208nm to 483 nm. The conformal autoencoders substantially outperform state-of-the-art methods and results for the first time in a promising prediction of polymer size from Raman spectra.
|
[
"['Eleni D. Koronaki' 'Luise F. Kaven' 'Johannes M. M. Faust'\n 'Ioannis G. Kevrekidis' 'Alexander Mitsos']"
] |
null | null |
2403.08386
| null | null |
http://arxiv.org/pdf/2403.08386v1
|
2024-03-13T09:49:26Z
|
2024-03-13T09:49:26Z
|
Optimizing Risk-averse Human-AI Hybrid Teams
|
We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans and AI systems. For hybrid teams, we will refer to both the humans and AI systems as agents. To improve team performance over that seen for agents operating individually, we propose a manager which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents. We further guide the manager's learning so they also minimize how many changes in delegation are made resulting from undesirable team behavior. We demonstrate the optimality of our manager's performance in several grid environments which include failure states which terminate an episode and should be avoided. We perform our experiments with teams of agents with varying degrees of acceptable risk, in the form of proximity to a failure state, and measure the manager's ability to make effective delegation decisions with respect to its own risk-based constraints, then compare these to the optimal decisions. Our results show our manager can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations.
|
[
"['Andrew Fuchs' 'Andrea Passarella' 'Marco Conti']"
] |
null | null |
2403.08403
| null | null |
http://arxiv.org/pdf/2403.08403v1
|
2024-03-13T10:37:52Z
|
2024-03-13T10:37:52Z
|
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo
Time-Series Data using Discrete Relaxation
|
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data. To address this challenge, we introduce a Deep Learning (DL)-based feature selection algorithm: Feature Selection through Discrete Relaxation (FSDR), tailored for PTS data. Unlike the existing feature selection algorithms, FSDR learns the important features as model parameters using discrete relaxation, which refers to the process of approximating a discrete optimisation problem with a continuous one. FSDR is capable of accommodating a high number of feature dimensions, a capability beyond the reach of existing DL-based or traditional methods. Through testing on a hyperspectral dataset (i.e., a type of PTS data), our experimental results demonstrate that FSDR outperforms three commonly used feature selection algorithms, taking into account a balance among execution time, $R^2$, and $RMSE$.
|
[
"['Mohammad Rahman' 'Manzur Murshed' 'Shyh Wei Teng' 'Manoranjan Paul']"
] |
null | null |
2403.08408
| null | null |
http://arxiv.org/pdf/2403.08408v1
|
2024-03-13T10:51:38Z
|
2024-03-13T10:51:38Z
|
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve
Generalization Performance of Deep Classification Models
|
The generalization performance of deep neural networks in classification tasks is a major concern in machine learning research. Despite widespread techniques used to diminish the over-fitting issue such as data augmentation, pseudo-labeling, regularization, and ensemble learning, this performance still needs to be enhanced with other approaches. In recent years, it has been theoretically demonstrated that the loss function characteristics i.e. its Lipschitzness and maximum value affect the generalization performance of deep neural networks which can be utilized as a guidance to propose novel distance measures. In this paper, by analyzing the aforementioned characteristics, we introduce a distance called Reduced Jeffries-Matusita as a loss function for training deep classification models to reduce the over-fitting issue. In our experiments, we evaluate the new loss function in two different problems: image classification in computer vision and node classification in the context of graph learning. The results show that the new distance measure stabilizes the training process significantly, enhances the generalization ability, and improves the performance of the models in the Accuracy and F1-score metrics, even if the training set size is small.
|
[
"['Mohammad Lashkari' 'Amin Gheibi']"
] |
null | null |
2403.08414
| null | null |
http://arxiv.org/pdf/2403.08414v1
|
2024-03-13T10:58:55Z
|
2024-03-13T10:58:55Z
|
Causal Graph Neural Networks for Wildfire Danger Prediction
|
Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires. In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning. The causal adjacency matrix considers the synergistic effect among variables and removes the spurious links from highly correlated impacts. Our methodology's effectiveness is demonstrated through superior performance forecasting wildfire patterns in the European boreal and mediterranean biome. The gain is especially prominent in a highly imbalanced dataset, showcasing an enhanced robustness of the model to adapt to regime shifts in functional relationships. Furthermore, SHAP values from our trained model further enhance our understanding of the model's inner workings.
|
[
"['Shan Zhao' 'Ioannis Prapas' 'Ilektra Karasante' 'Zhitong Xiong'\n 'Ioannis Papoutsis' 'Gustau Camps-Valls' 'Xiao Xiang Zhu']"
] |
null | null |
2403.08417
| null | null |
http://arxiv.org/pdf/2403.08417v1
|
2024-03-13T11:05:40Z
|
2024-03-13T11:05:40Z
|
The Development and Performance of a Machine Learning Based Mobile
Platform for Visually Determining the Etiology of Penile Pathology
|
Machine-learning algorithms can facilitate low-cost, user-guided visual diagnostic platforms for addressing disparities in access to sexual health services. We developed a clinical image dataset using original and augmented images for five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts. We used a U-net architecture model for semantic pixel segmentation into background or subject image, the Inception-ResNet version 2 neural architecture to classify each pixel as diseased or non-diseased, and a salience map using GradCAM++. We trained the model on a random 91% sample of the image database using 150 epochs per image, and evaluated the model on the remaining 9% of images, assessing recall (or sensitivity), precision, specificity, and F1-score (accuracy). Of the 239 images in the validation dataset, 45 (18.8%) were of genital warts, 43 (18.0%) were of HSV infection, 29 (12.1%) were of penile cancer, 40 (16.7%) were of penile candidiasis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were of non-diseased penises. The overall accuracy of the model for correctly classifying the diseased image was 0.944. Between July 1st and October 1st 2023, there were 2,640 unique users of the mobile platform. Among a random sample of submissions (n=437), 271 (62.0%) were from the United States, 64 (14.6%) from Singapore, 41 (9.4%) from Candia, 40 (9.2%) from the United Kingdom, and 21 (4.8%) from Vietnam. The majority (n=277 [63.4%]) were between 18 and 30 years old. We report on the development of a machine-learning model for classifying five penile diseases, which demonstrated excellent performance on a validation dataset. That model is currently in use globally and has the potential to improve access to diagnostic services for penile diseases.
|
[
"['Lao-Tzu Allan-Blitz' 'Sithira Ambepitiya' 'Raghavendra Tirupathi'\n 'Jeffrey D. Klausner' 'Yudara Kularathne']"
] |
null | null |
2403.08428
| null | null |
http://arxiv.org/pdf/2403.08428v1
|
2024-03-13T11:26:43Z
|
2024-03-13T11:26:43Z
|
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued
Neural Networks
|
Deep Neural Networks are widely used in academy as well as corporate and public applications, including safety critical applications such as health care and autonomous driving. The ability to explain their output is critical for safety reasons as well as acceptance among applicants. A multitude of methods have been proposed to explain real-valued neural networks. Recently, complex-valued neural networks have emerged as a new class of neural networks dealing with complex-valued input data without the necessity of projecting them onto $mathbb{R}^2$. This brings up the need to develop explanation algorithms for this kind of neural networks. In this paper we provide these developments. While we focus on adapting the widely used DeepSHAP algorithm to the complex domain, we also present versions of four gradient based explanation methods suitable for use in complex-valued neural networks. We evaluate the explanation quality of all presented algorithms and provide all of them as an open source library adaptable to most recent complex-valued neural network architectures.
|
[
"['Florian Eilers' 'Xiaoyi Jiang']"
] |
null | null |
2403.08438
| null | null |
http://arxiv.org/pdf/2403.08438v2
|
2024-03-19T10:54:22Z
|
2024-03-13T11:44:30Z
|
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation
on Graph Neural Network Research
|
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained on.
|
[
"['Tobias Hille' 'Maximilian Stubbemann' 'Tom Hanika']"
] |
null | null |
2403.08444
| null | null |
http://arxiv.org/pdf/2403.08444v1
|
2024-03-13T11:56:10Z
|
2024-03-13T11:56:10Z
|
COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud
Environments
|
In this work, we present COSTREAM, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming query in an edge-cloud environment. The cost model can be used to find an initial placement of operators across heterogeneous hardware, which is particularly important in these environments. In our evaluation, we demonstrate that COSTREAM can produce highly accurate cost estimates for the initial operator placement and even generalize to unseen placements, queries, and hardware. When using COSTREAM to optimize the placements of streaming operators, a median speed-up of around 21x can be achieved compared to baselines.
|
[
"['Roman Heinrich' 'Carsten Binnig' 'Harald Kornmayer' 'Manisha Luthra']"
] |
null | null |
2403.08448
| null | null |
http://arxiv.org/pdf/2403.08448v1
|
2024-03-13T12:03:27Z
|
2024-03-13T12:03:27Z
|
Actor-Critic Physics-informed Neural Lyapunov Control
|
Designing control policies for stabilization tasks with provable guarantees is a long-standing problem in nonlinear control. A crucial performance metric is the size of the resulting region of attraction, which essentially serves as a robustness "margin" of the closed-loop system against uncertainties. In this paper, we propose a new method to train a stabilizing neural network controller along with its corresponding Lyapunov certificate, aiming to maximize the resulting region of attraction while respecting the actuation constraints. Crucial to our approach is the use of Zubov's Partial Differential Equation (PDE), which precisely characterizes the true region of attraction of a given control policy. Our framework follows an actor-critic pattern where we alternate between improving the control policy (actor) and learning a Zubov function (critic). Finally, we compute the largest certifiable region of attraction by invoking an SMT solver after the training procedure. Our numerical experiments on several design problems show consistent and significant improvements in the size of the resulting region of attraction.
|
[
"['Jiarui Wang' 'Mahyar Fazlyab']"
] |
null | null |
2403.08462
| null | null |
http://arxiv.org/pdf/2403.08462v1
|
2024-03-13T12:25:47Z
|
2024-03-13T12:25:47Z
|
Authorship Verification based on the Likelihood Ratio of Grammar Models
|
Authorship Verification (AV) is the process of analyzing a set of documents to determine whether they were written by a specific author. This problem often arises in forensic scenarios, e.g., in cases where the documents in question constitute evidence for a crime. Existing state-of-the-art AV methods use computational solutions that are not supported by a plausible scientific explanation for their functioning and that are often difficult for analysts to interpret. To address this, we propose a method relying on calculating a quantity we call $lambda_G$ (LambdaG): the ratio between the likelihood of a document given a model of the Grammar for the candidate author and the likelihood of the same document given a model of the Grammar for a reference population. These Grammar Models are estimated using $n$-gram language models that are trained solely on grammatical features. Despite not needing large amounts of data for training, LambdaG still outperforms other established AV methods with higher computational complexity, including a fine-tuned Siamese Transformer network. Our empirical evaluation based on four baseline methods applied to twelve datasets shows that LambdaG leads to better results in terms of both accuracy and AUC in eleven cases and in all twelve cases if considering only topic-agnostic methods. The algorithm is also highly robust to important variations in the genre of the reference population in many cross-genre comparisons. In addition to these properties, we demonstrate how LambdaG is easier to interpret than the current state-of-the-art. We argue that the advantage of LambdaG over other methods is due to fact that it is compatible with Cognitive Linguistic theories of language processing.
|
[
"['Andrea Nini' 'Oren Halvani' 'Lukas Graner' 'Valerio Gherardi'\n 'Shunichi Ishihara']"
] |
null | null |
2403.08464
| null | null |
http://arxiv.org/pdf/2403.08464v1
|
2024-03-13T12:26:55Z
|
2024-03-13T12:26:55Z
|
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
|
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approaches may compromise critical information during pathology removal, leading to restorations that do not align with unaffected regions in the original scans. Such discrepancies can inadvertently increase false positive rates and reduce specificity, complicating radiological evaluations. This paper introduces Temporal Harmonization for Optimal Restoration (THOR), which refines the de-noising process by integrating implicit guidance through temporal anomaly maps. THOR aims to preserve the integrity of healthy tissue in areas unaffected by pathology. Comparative evaluations show that THOR surpasses existing diffusion-based methods in detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/ci-ber/THOR_DDPM.
|
[
"['Cosmin I. Bercea' 'Benedikt Wiestler' 'Daniel Rueckert'\n 'Julia A. Schnabel']"
] |
null | null |
2403.08469
| null | null |
http://arxiv.org/pdf/2403.08469v1
|
2024-03-13T12:31:08Z
|
2024-03-13T12:31:08Z
|
An Analysis of Human Alignment of Latent Diffusion Models
|
Diffusion models, trained on large amounts of data, showed remarkable performance for image synthesis. They have high error consistency with humans and low texture bias when used for classification. Furthermore, prior work demonstrated the decomposability of their bottleneck layer representations into semantic directions. In this work, we analyze how well such representations are aligned to human responses on a triplet odd-one-out task. We find that despite the aforementioned observations: I) The representational alignment with humans is comparable to that of models trained only on ImageNet-1k. II) The most aligned layers of the denoiser U-Net are intermediate layers and not the bottleneck. III) Text conditioning greatly improves alignment at high noise levels, hinting at the importance of abstract textual information, especially in the early stage of generation.
|
[
"['Lorenz Linhardt' 'Marco Morik' 'Sidney Bender' 'Naima Elosegui Borras']"
] |
null | null |
2403.08477
| null | null |
http://arxiv.org/pdf/2403.08477v3
|
2024-07-01T15:29:16Z
|
2024-03-13T12:46:03Z
|
Unleashing the Power of Meta-tuning for Few-shot Generalization Through
Sparse Interpolated Experts
|
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning. In trying to harness the best of both worlds, meta-tuning introduces a subsequent optimization stage of foundation models but has so far only shown limited success and crucially tends to underperform on out-of-distribution (OOD) tasks. In this paper, we introduce Sparse MetA-Tuning (SMAT), a method inspired by sparse mixture-of-experts approaches and trained to isolate subsets of pre-trained parameters automatically for meta-tuning on each task. SMAT successfully overcomes OOD sensitivity and delivers on the promise of enhancing the transfer abilities of vision foundation models beyond parameter-efficient fine-tuning. We establish new state-of-the-art results on a challenging combination of Meta-Dataset augmented with additional OOD tasks in both zero-shot and gradient-based adaptation settings. In addition, we provide a thorough analysis of the superiority of learned over hand-designed sparsity patterns for sparse expert methods and the pivotal importance of the sparsity level in balancing between in-distribution and out-of-distribution generalization. Our code is publicly available.
|
[
"['Shengzhuang Chen' 'Jihoon Tack' 'Yunqiao Yang' 'Yee Whye Teh'\n 'Jonathan Richard Schwarz' 'Ying Wei']"
] |
null | null |
2403.08481
| null | null |
http://arxiv.org/pdf/2403.08481v1
|
2024-03-13T12:46:51Z
|
2024-03-13T12:46:51Z
|
SoK: Reducing the Vulnerability of Fine-tuned Language Models to
Membership Inference Attacks
|
Natural language processing models have experienced a significant upsurge in recent years, with numerous applications being built upon them. Many of these applications require fine-tuning generic base models on customized, proprietary datasets. This fine-tuning data is especially likely to contain personal or sensitive information about individuals, resulting in increased privacy risk. Membership inference attacks are the most commonly employed attack to assess the privacy leakage of a machine learning model. However, limited research is available on the factors that affect the vulnerability of language models to this kind of attack, or on the applicability of different defense strategies in the language domain. We provide the first systematic review of the vulnerability of fine-tuned large language models to membership inference attacks, the various factors that come into play, and the effectiveness of different defense strategies. We find that some training methods provide significantly reduced privacy risk, with the combination of differential privacy and low-rank adaptors achieving the best privacy protection against these attacks.
|
[
"['Guy Amit' 'Abigail Goldsteen' 'Ariel Farkash']"
] |
null | null |
2403.08506
| null | null |
http://arxiv.org/pdf/2403.08506v1
|
2024-03-11T15:58:15Z
|
2024-03-11T15:58:15Z
|
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain
Generalization in Federated Learning
|
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However, most existing FL methods assume that domain labels are provided during training, and their evaluation imposes explicit constraints on the number of domains, which must strictly match the number of clients. Because of the underutilization of numerous edge devices and additional cross-client domain annotations in the real world, such restrictions may be impractical and involve potential privacy leaks. In this paper, we propose an efficient and novel approach, called Disentangled Prompt Tuning (DiPrompT), a method that tackles the above restrictions by learning adaptive prompts for domain generalization in a distributed manner. Specifically, we first design two types of prompts, i.e., global prompt to capture general knowledge across all clients and domain prompts to capture domain-specific knowledge. They eliminate the restriction on the one-to-one mapping between source domains and local clients. Furthermore, a dynamic query metric is introduced to automatically search the suitable domain label for each sample, which includes two-substep text-image alignments based on prompt tuning without labor-intensive annotation. Extensive experiments on multiple datasets demonstrate that our DiPrompT achieves superior domain generalization performance over state-of-the-art FL methods when domain labels are not provided, and even outperforms many centralized learning methods using domain labels.
|
[
"['Sikai Bai' 'Jie Zhang' 'Shuaicheng Li' 'Song Guo' 'Jingcai Guo'\n 'Jun Hou' 'Tao Han' 'Xiaocheng Lu']"
] |
null | null |
2403.08525
| null | null |
http://arxiv.org/pdf/2403.08525v1
|
2024-03-13T13:33:35Z
|
2024-03-13T13:33:35Z
|
From Weak to Strong Sound Event Labels using Adaptive Change-Point
Detection and Active Learning
|
In this work we propose an audio recording segmentation method based on an adaptive change point detection (A-CPD) for machine guided weak label annotation of audio recording segments. The goal is to maximize the amount of information gained about the temporal activation's of the target sounds. For each unlabeled audio recording, we use a prediction model to derive a probability curve used to guide annotation. The prediction model is initially pre-trained on available annotated sound event data with classes that are disjoint from the classes in the unlabeled dataset. The prediction model then gradually adapts to the annotations provided by the annotator in an active learning loop. The queries used to guide the weak label annotator towards strong labels are derived using change point detection on these probabilities. We show that it is possible to derive strong labels of high quality even with a limited annotation budget, and show favorable results for A-CPD when compared to two baseline query strategies.
|
[
"['John Martinsson' 'Olof Mogren' 'Maria Sandsten' 'Tuomas Virtanen']"
] |
null | null |
2403.08536
| null | null |
http://arxiv.org/abs/2403.08536v1
|
2024-03-13T13:51:02Z
|
2024-03-13T13:51:02Z
|
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional
Image Classifiers
|
Convolutional Neural Networks (CNNs) are nowadays the model of choice in Computer Vision, thanks to their ability to automatize the feature extraction process in visual tasks. However, the knowledge acquired during training is fully subsymbolic, and hence difficult to understand and explain to end users. In this paper, we propose a new technique called HOLMES (HOLonym-MEronym based Semantic inspection) that decomposes a label into a set of related concepts, and provides component-level explanations for an image classification model. Specifically, HOLMES leverages ontologies, web scraping and transfer learning to automatically construct meronym (parts)-based detectors for a given holonym (class). Then, it produces heatmaps at the meronym level and finally, by probing the holonym CNN with occluded images, it highlights the importance of each part on the classification output. Compared to state-of-the-art saliency methods, HOLMES takes a step further and provides information about both where and what the holonym CNN is looking at, without relying on densely annotated datasets and without forcing concepts to be associated to single computational units. Extensive experimental evaluation on different categories of objects (animals, tools and vehicles) shows the feasibility of our approach. On average, HOLMES explanations include at least two meronyms, and the ablation of a single meronym roughly halves the holonym model confidence. The resulting heatmaps were quantitatively evaluated using the deletion/insertion/preservation curves. All metrics were comparable to those achieved by GradCAM, while offering the advantage of further decomposing the heatmap in human-understandable concepts, thus highlighting both the relevance of meronyms to object classification, as well as HOLMES ability to capture it. The code is available at https://github.com/FrancesC0de/HOLMES.
|
[
"['Francesco Dibitonto' 'Fabio Garcea' 'André Panisson' 'Alan Perotti'\n 'Lia Morra']"
] |
null | null |
2403.08540
| null | null |
http://arxiv.org/pdf/2403.08540v2
|
2024-06-14T20:21:05Z
|
2024-03-13T13:54:00Z
|
Language models scale reliably with over-training and on downstream
tasks
|
Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps between current scaling studies and how language models are ultimately trained and evaluated. For instance, scaling is usually studied in the compute-optimal training regime (i.e., "Chinchilla optimal" regime). In contrast, models are often over-trained to reduce inference costs. Moreover, scaling laws mostly predict loss on next-token prediction, but models are usually compared on downstream task performance. To address both shortcomings, we create a testbed of 104 models with 0.011B to 6.9B parameters trained with various numbers of tokens on three data distributions. First, we fit scaling laws that extrapolate in both the amount of over-training and the number of model parameters. This enables us to predict the validation loss of a 1.4B parameter, 900B token run (i.e., 32$times$ over-trained) and a 6.9B parameter, 138B token run (i.e., a compute-optimal run)$unicode{x2014}$each from experiments that take 300$times$ less compute. Second, we relate the perplexity of a language model to its downstream task performance by proposing a power law. We use this law to predict top-1 error averaged over downstream tasks for the two aforementioned models, using experiments that take 20$times$ less compute. Our experiments are available at https://github.com/mlfoundations/scaling.
|
[
"['Samir Yitzhak Gadre' 'Georgios Smyrnis' 'Vaishaal Shankar'\n 'Suchin Gururangan' 'Mitchell Wortsman' 'Rulin Shao' 'Jean Mercat'\n 'Alex Fang' 'Jeffrey Li' 'Sedrick Keh' 'Rui Xin' 'Marianna Nezhurina'\n 'Igor Vasiljevic' 'Jenia Jitsev' 'Luca Soldaini' 'Alexandros G. Dimakis'\n 'Gabriel Ilharco' 'Pang Wei Koh' 'Shuran Song' 'Thomas Kollar'\n 'Yair Carmon' 'Achal Dave' 'Reinhard Heckel' 'Niklas Muennighoff'\n 'Ludwig Schmidt']"
] |
null | null |
2403.08550
| null | null |
http://arxiv.org/pdf/2403.08550v1
|
2024-03-13T14:02:42Z
|
2024-03-13T14:02:42Z
|
CINA: Conditional Implicit Neural Atlas for Spatio-Temporal
Representation of Fetal Brains
|
We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain, that is fully independent of affine or non-rigid registration. During training, CINA learns a general representation of the fetal brain and encodes subject specific information into latent code. After training, CINA can construct a faithful atlas with tissue probability maps of the fetal brain for any gestational age (GA) and anatomical variation covered within the training domain. Thus, CINA is competent to represent both, neurotypical and pathological brains. Furthermore, a trained CINA model can be fit to brain MRI of unseen subjects via test-time optimization of the latent code. CINA can then produce probabilistic tissue maps tailored to a particular subject. We evaluate our method on a total of 198 T2 weighted MRI of normal and abnormal fetal brains from the dHCP and FeTA datasets. We demonstrate CINA's capability to represent a fetal brain atlas that can be flexibly conditioned on GA and on anatomical variations like ventricular volume or degree of cortical folding, making it a suitable tool for modeling both neurotypical and pathological brains. We quantify the fidelity of our atlas by means of tissue segmentation and age prediction and compare it to an established baseline. CINA demonstrates superior accuracy for neurotypical brains and pathological brains with ventriculomegaly. Moreover, CINA scores a mean absolute error of 0.23 weeks in fetal brain age prediction, further confirming an accurate representation of fetal brain development.
|
[
"['Maik Dannecker' 'Vanessa Kyriakopoulou' 'Lucilio Cordero-Grande'\n 'Anthony N. Price' 'Joseph V. Hajnal' 'Daniel Rueckert']"
] |
null | null |
2403.08553
| null | null |
http://arxiv.org/pdf/2403.08553v1
|
2024-03-13T14:06:18Z
|
2024-03-13T14:06:18Z
|
Regret Analysis of Policy Optimization over Submanifolds for Linearly
Constrained Online LQG
|
Recent advancement in online optimization and control has provided novel tools to study online linear quadratic regulator (LQR) problems, where cost matrices are varying adversarially over time. However, the controller parameterization of existing works may not satisfy practical conditions like sparsity due to physical connections. In this work, we study online linear quadratic Gaussian problems with a given linear constraint imposed on the controller. Inspired by the recent work of [1] which proposed, for a linearly constrained policy optimization of an offline LQR, a second order method equipped with a Riemannian metric that emerges naturally in the context of optimal control problems, we propose online optimistic Newton on manifold (OONM) which provides an online controller based on the prediction on the first and second order information of the function sequence. To quantify the proposed algorithm, we leverage the notion of regret defined as the sub-optimality of its cumulative cost to that of a (locally) minimizing controller sequence and provide the regret bound in terms of the path-length of the minimizer sequence. Simulation results are also provided to verify the property of OONM.
|
[
"['Ting-Jui Chang' 'Shahin Shahrampour']"
] |
null | null |
2403.08554
| null | null |
http://arxiv.org/pdf/2403.08554v1
|
2024-03-13T14:06:51Z
|
2024-03-13T14:06:51Z
|
Federated Knowledge Graph Unlearning via Diffusion Model
|
Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy. Knowledge graph (KG) embedding representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated KG embedding enables the utilization of knowledge from diverse client sources while safeguarding the privacy of local data. However, due to demands such as privacy protection and the need to adapt to dynamic data changes, investigations into machine unlearning (MU) have been sparked. However, it is challenging to maintain the performance of KG embedding models while forgetting the influence of specific forgotten data on the model. In this paper, we propose FedDM, a novel framework tailored for machine unlearning in federated knowledge graphs. Leveraging diffusion models, we generate noisy data to sensibly mitigate the influence of specific knowledge on FL models while preserving the overall performance concerning the remaining data. We conduct experimental evaluations on benchmark datasets to assess the efficacy of the proposed model. Extensive experiments demonstrate that FedDM yields promising results in knowledge forgetting.
|
[
"['Bingchen Liu' 'Yuanyuan Fang']"
] |
null | null |
2403.08562
| null | null |
http://arxiv.org/pdf/2403.08562v1
|
2024-03-13T14:14:47Z
|
2024-03-13T14:14:47Z
|
Structural perspective on constraint-based learning of Markov networks
|
Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-based structure learning, which entails learning the undirected graph from data through the execution of conditional independence tests. We establish theoretical limits concerning two critical aspects of constraint-based learning of Markov networks: the number of tests and the sizes of the conditioning sets. These bounds uncover an exciting interplay between the structural properties of the graph and the amount of tests required to learn a Markov network. The starting point of our work is that the graph parameter maximum pairwise connectivity, $kappa$, that is, the maximum number of vertex-disjoint paths connecting a pair of vertices in the graph, is responsible for the sizes of independence tests required to learn the graph. On one hand, we show that at least one test with the size of the conditioning set at least $kappa$ is always necessary. On the other hand, we prove that any graph can be learned by performing tests of size at most $kappa$. This completely resolves the question of the minimum size of conditioning sets required to learn the graph. When it comes to the number of tests, our upper bound on the sizes of conditioning sets implies that every $n$-vertex graph can be learned by at most $n^{kappa}$ tests with conditioning sets of sizes at most $kappa$. We show that for any upper bound $q$ on the sizes of the conditioning sets, there exist graphs with $O(n q)$ vertices that require at least $n^{Omega(kappa)}$ tests to learn. This lower bound holds even when the treewidth and the maximum degree of the graph are at most $kappa+2$. On the positive side, we prove that every graph of bounded treewidth can be learned by a polynomial number of tests with conditioning sets of sizes at most $2kappa$.
|
[
"['Tuukka Korhonen' 'Fedor V. Fomin' 'Pekka Parviainen']"
] |
null | null |
2403.08568
| null | null |
http://arxiv.org/pdf/2403.08568v2
|
2024-03-14T12:26:17Z
|
2024-03-13T14:24:09Z
|
Consistent Prompting for Rehearsal-Free Continual Learning
|
Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment, leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper, we propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing. Specifically, all existing classifiers are exposed to prompt training, resulting in classifier consistency learning. In addition, prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing.
|
[
"['Zhanxin Gao' 'Jun Cen' 'Xiaobin Chang']"
] |
null | null |
2403.08569
| null | null |
http://arxiv.org/pdf/2403.08569v1
|
2024-03-13T14:25:15Z
|
2024-03-13T14:25:15Z
|
A Physics-driven GraphSAGE Method for Physical Process Simulations
Described by Partial Differential Equations
|
Physics-informed neural networks (PINNs) have successfully addressed various computational physics problems based on partial differential equations (PDEs). However, while tackling issues related to irregularities like singularities and oscillations, trained solutions usually suffer low accuracy. In addition, most current works only offer the trained solution for predetermined input parameters. If any change occurs in input parameters, transfer learning or retraining is required, and traditional numerical techniques also need an independent simulation. In this work, a physics-driven GraphSAGE approach (PD-GraphSAGE) based on the Galerkin method and piecewise polynomial nodal basis functions is presented to solve computational problems governed by irregular PDEs and to develop parametric PDE surrogate models. This approach employs graph representations of physical domains, thereby reducing the demands for evaluated points due to local refinement. A distance-related edge feature and a feature mapping strategy are devised to help training and convergence for singularity and oscillation situations, respectively. The merits of the proposed method are demonstrated through a couple of cases. Moreover, the robust PDE surrogate model for heat conduction problems parameterized by the Gaussian random field source is successfully established, which not only provides the solution accurately but is several times faster than the finite element method in our experiments.
|
[
"['Hang Hu' 'Sidi Wu' 'Guoxiong Cai' 'Na Liu']"
] |
null | null |
2403.08572
| null | null |
http://arxiv.org/pdf/2403.08572v1
|
2024-03-13T14:28:02Z
|
2024-03-13T14:28:02Z
|
Caformer: Rethinking Time Series Analysis from Causal Perspective
|
Time series analysis is a vital task with broad applications in various domains. However, effectively capturing cross-dimension and cross-time dependencies in non-stationary time series poses significant challenges, particularly in the context of environmental factors. The spurious correlation induced by the environment confounds the causal relationships between cross-dimension and cross-time dependencies. In this paper, we introduce a novel framework called Caformer (underline{textbf{Ca}}usal Transunderline{textbf{former}}) for time series analysis from a causal perspective. Specifically, our framework comprises three components: Dynamic Learner, Environment Learner, and Dependency Learner. The Dynamic Learner unveils dynamic interactions among dimensions, the Environment Learner mitigates spurious correlations caused by environment with a back-door adjustment, and the Dependency Learner aims to infer robust interactions across both time and dimensions. Our Caformer demonstrates consistent state-of-the-art performance across five mainstream time series analysis tasks, including long- and short-term forecasting, imputation, classification, and anomaly detection, with proper interpretability.
|
[
"['Kexuan Zhang' 'Xiaobei Zou' 'Yang Tang']"
] |
null | null |
2403.08579
| null | null |
http://arxiv.org/pdf/2403.08579v3
|
2024-05-08T07:07:25Z
|
2024-03-13T14:34:34Z
|
Machine Learning Optimized Orthogonal Basis Piecewise Polynomial
Approximation
|
Piecewise Polynomials (PPs) are utilized in several engineering disciplines, like trajectory planning, to approximate position profiles given in the form of a set of points. While the approximation target along with domain-specific requirements, like Ck -continuity, can be formulated as a system of equations and a result can be computed directly, such closed-form solutions posses limited flexibility with respect to polynomial degrees, polynomial bases or adding further domain-specific requirements. Sufficiently complex optimization goals soon call for the use of numerical methods, like gradient descent. Since gradient descent lies at the heart of training Artificial Neural Networks (ANNs), modern Machine Learning (ML) frameworks like TensorFlow come with a set of gradient-based optimizers potentially suitable for a wide range of optimization problems beyond the training task for ANNs. Our approach is to utilize the versatility of PP models and combine it with the potential of modern ML optimizers for the use in function approximation in 1D trajectory planning in the context of electronic cam design. We utilize available optimizers of the ML framework TensorFlow directly, outside of the scope of ANNs, to optimize model parameters of our PP model. In this paper, we show how an orthogonal polynomial basis contributes to improving approximation and continuity optimization performance. Utilizing Chebyshev polynomials of the first kind, we develop a novel regularization approach enabling clearly improved convergence behavior. We show that, using this regularization approach, Chebyshev basis performs better than power basis for all relevant optimizers in the combined approximation and continuity optimization setting and demonstrate usability of the presented approach within the electronic cam domain.
|
[
"['Hannes Waclawek' 'Stefan Huber']"
] |
null | null |
2403.08584
| null | null |
http://arxiv.org/pdf/2403.08584v1
|
2024-03-13T14:37:00Z
|
2024-03-13T14:37:00Z
|
Local Binary and Multiclass SVMs Trained on a Quantum Annealer
|
Support vector machines (SVMs) are widely used machine learning models (e.g., in remote sensing), with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM models characterised by quantum training and classical execution have been introduced. These models have demonstrated comparable performance to their classical counterparts. However, they are limited in the training set size due to the restricted connectivity of the current quantum annealers. Hence, to take advantage of large datasets (like those related to Earth observation), a strategy is required. In the classical domain, local SVMs, namely, SVMs trained on the data samples selected by a k-nearest neighbors model, have already proven successful. Here, the local application of quantum-trained SVM models is proposed and empirically assessed. In particular, this approach allows overcoming the constraints on the training set size of the quantum-trained models while enhancing their performance. In practice, the FaLK-SVM method, designed for efficient local SVMs, has been combined with quantum-trained SVM models for binary and multiclass classification. In addition, for comparison, FaLK-SVM has been interfaced for the first time with a classical single-step multiclass SVM model (CS SVM). Concerning the empirical evaluation, D-Wave's quantum annealers and real-world datasets taken from the remote sensing domain have been employed. The results have shown the effectiveness and scalability of the proposed approach, but also its practical applicability in a real-world large-scale scenario.
|
[
"['Enrico Zardini' 'Amer Delilbasic' 'Enrico Blanzieri'\n 'Gabriele Cavallaro' 'Davide Pastorello']"
] |
null | null |
2403.08585
| null | null |
http://arxiv.org/pdf/2403.08585v3
|
2024-05-26T06:17:59Z
|
2024-03-13T14:42:06Z
|
Improving Implicit Regularization of SGD with Preconditioning for Least
Square Problems
|
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice and plays an important role in the generalization of modern machine learning. However, prior research has revealed instances where the generalization performance of SGD is worse than ridge regression due to uneven optimization along different dimensions. Preconditioning offers a natural solution to this issue by rebalancing optimization across different directions. Yet, the extent to which preconditioning can enhance the generalization performance of SGD and whether it can bridge the existing gap with ridge regression remains uncertain. In this paper, we study the generalization performance of SGD with preconditioning for the least squared problem. We make a comprehensive comparison between preconditioned SGD and (standard & preconditioned) ridge regression. Our study makes several key contributions toward understanding and improving SGD with preconditioning. First, we establish excess risk bounds (generalization performance) for preconditioned SGD and ridge regression under an arbitrary preconditions matrix. Second, leveraging the excessive risk characterization of preconditioned SGD and ridge regression, we show that (through construction) there exists a simple preconditioned matrix that can make SGD comparable to (standard & preconditioned) ridge regression. Finally, we show that our proposed preconditioning matrix is straightforward enough to allow robust estimation from finite samples while maintaining a theoretical improvement. Our empirical results align with our theoretical findings, collectively showcasing the enhanced regularization effect of preconditioned SGD.
|
[
"['Junwei Su' 'Difan Zou' 'Chuan Wu']"
] |
null | null |
2403.08589
| null | null |
http://arxiv.org/pdf/2403.08589v1
|
2024-03-13T14:51:16Z
|
2024-03-13T14:51:16Z
|
Can physical information aid the generalization ability of Neural
Networks for hydraulic modeling?
|
Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks proved to lack predictive capabilities. In this work, we propose to mitigate such problem by introducing physical information into the training phase. The idea is borrowed from Physics-Informed Neural Networks which have been recently proposed in other contexts. Physics-Informed Neural Networks embed physical information in the form of the residual of the Partial Differential Equations (PDEs) governing the phenomenon and, as such, are conceived as neural solvers, i.e. an alternative to traditional numerical solvers. Such approach is seldom suitable for environmental hydraulics, where epistemic uncertainties are large, and computing residuals of PDEs exhibits difficulties similar to those faced by classical numerical methods. Instead, we envisaged the employment of Neural Networks as neural operators, featuring physical constraints formulated without resorting to PDEs. The proposed novel methodology shares similarities with data augmentation and regularization. We show that incorporating such soft physical information can improve predictive capabilities.
|
[
"['Gianmarco Guglielmo' 'Andrea Montessori' 'Jean-Michel Tucny'\n 'Michele La Rocca' 'Pietro Prestininzi']"
] |
null | null |
2403.08592
| null | null |
http://arxiv.org/pdf/2403.08592v1
|
2024-03-13T14:57:10Z
|
2024-03-13T14:57:10Z
|
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
|
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.
|
[
"['Niklas Grieger' 'Siamak Mehrkanoon' 'Stephan Bialonski']"
] |
null | null |
2403.08609
| null | null |
http://arxiv.org/pdf/2403.08609v2
|
2024-03-14T10:01:45Z
|
2024-03-13T15:21:14Z
|
On the Convergence of Locally Adaptive and Scalable Diffusion-Based
Sampling Methods for Deep Bayesian Neural Network Posteriors
|
Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging where it is necessary to assess the reliability of a neural network's prediction. Bayesian neural networks are a promising approach for modeling uncertainties in deep neural networks. Unfortunately, generating samples from the posterior distribution of neural networks is a major challenge. One significant advance in that direction would be the incorporation of adaptive step sizes, similar to modern neural network optimizers, into Monte Carlo Markov chain sampling algorithms without significantly increasing computational demand. Over the past years, several papers have introduced sampling algorithms with claims that they achieve this property. However, do they indeed converge to the correct distribution? In this paper, we demonstrate that these methods can have a substantial bias in the distribution they sample, even in the limit of vanishing step sizes and at full batch size.
|
[
"['Tim Rensmeyer' 'Oliver Niggemann']"
] |
null | null |
2403.08613
| null | null |
http://arxiv.org/pdf/2403.08613v1
|
2024-03-13T15:23:55Z
|
2024-03-13T15:23:55Z
|
Link Prediction for Social Networks using Representation Learning and
Heuristic-based Features
|
The exponential growth in scale and relevance of social networks enable them to provide expansive insights. Predicting missing links in social networks efficiently can help in various modern-day business applications ranging from generating recommendations to influence analysis. Several categories of solutions exist for the same. Here, we explore various feature extraction techniques to generate representations of nodes and edges in a social network that allow us to predict missing links. We compare the results of using ten feature extraction techniques categorized across Structural embeddings, Neighborhood-based embeddings, Graph Neural Networks, and Graph Heuristics, followed by modeling with ensemble classifiers and custom Neural Networks. Further, we propose combining heuristic-based features and learned representations that demonstrate improved performance for the link prediction task on social network datasets. Using this method to generate accurate recommendations for many applications is a matter of further study that appears very promising. The code for all the experiments has been made public.
|
[
"['Samarth Khanna' 'Sree Bhattacharyya' 'Sudipto Ghosh' 'Kushagra Agarwal'\n 'Asit Kumar Das']"
] |
null | null |
2403.08618
| null | null |
http://arxiv.org/pdf/2403.08618v1
|
2024-03-13T15:32:08Z
|
2024-03-13T15:32:08Z
|
Verifix: Post-Training Correction to Improve Label Noise Robustness with
Verified Samples
|
Label corruption, where training samples have incorrect labels, can significantly degrade the performance of machine learning models. This corruption often arises from non-expert labeling or adversarial attacks. Acquiring large, perfectly labeled datasets is costly, and retraining large models from scratch when a clean dataset becomes available is computationally expensive. To address this challenge, we propose Post-Training Correction, a new paradigm that adjusts model parameters after initial training to mitigate label noise, eliminating the need for retraining. We introduce Verifix, a novel Singular Value Decomposition (SVD) based algorithm that leverages a small, verified dataset to correct the model weights using a single update. Verifix uses SVD to estimate a Clean Activation Space and then projects the model's weights onto this space to suppress activations corresponding to corrupted data. We demonstrate Verifix's effectiveness on both synthetic and real-world label noise. Experiments on the CIFAR dataset with 25% synthetic corruption show 7.36% generalization improvements on average. Additionally, we observe generalization improvements of up to 2.63% on naturally corrupted datasets like WebVision1.0 and Clothing1M.
|
[
"['Sangamesh Kodge' 'Deepak Ravikumar' 'Gobinda Saha' 'Kaushik Roy']"
] |
null | null |
2403.08627
| null | null |
http://arxiv.org/pdf/2403.08627v2
|
2024-07-01T20:11:32Z
|
2024-03-13T15:40:17Z
|
Multifidelity linear regression for scientific machine learning from
scarce data
|
Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulation is expensive. However, in many scientific and engineering settings, generating high-fidelity data on which to train ML models is expensive, and the available budget for generating training data is limited, so that high-fidelity training data are scarce. ML models trained on scarce data have high variance, resulting in poor expected generalization performance. We propose a new multifidelity training approach for scientific machine learning via linear regression that exploits the scientific context where data of varying fidelities and costs are available: for example, high-fidelity data may be generated by an expensive fully resolved physics simulation whereas lower-fidelity data may arise from a cheaper model based on simplifying assumptions. We use the multifidelity data within an approximate control variate framework to define new multifidelity Monte Carlo estimators for linear regression models. We provide bias and variance analysis of our new estimators that guarantee the approach's accuracy and improved robustness to scarce high-fidelity data. Numerical results demonstrate that our multifidelity training approach achieves similar accuracy to the standard high-fidelity only approach with orders-of-magnitude reduced high-fidelity data requirements.
|
[
"['Elizabeth Qian' 'Dayoung Kang' 'Vignesh Sella' 'Anirban Chaudhuri']"
] |
null | null |
2403.08630
| null | null |
http://arxiv.org/pdf/2403.08630v1
|
2024-03-13T15:45:29Z
|
2024-03-13T15:45:29Z
|
Leveraging Non-Decimated Wavelet Packet Features and Transformer Models
for Time Series Forecasting
|
This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with different numbers of vanishing moments as input features to both non-temporal and temporal forecasting methods, by selecting these numbers during the cross-validation phase. Secondly, we compare the use of both the non-decimated wavelet transform and the non-decimated wavelet packet transform for computing these features, the latter providing a much larger set of potentially useful coefficient vectors. The wavelet coefficients are computed using a shifted version of the typical pyramidal algorithm to ensure no leakage of future information into these inputs. Thirdly, we evaluate the use of these wavelet features on a significantly wider set of forecasting methods than previous studies, including both temporal and non-temporal models, and both statistical and deep learning-based methods. The latter include state-of-the-art transformer-based neural network architectures. Our experiments suggest significant benefit in replacing higher-order lagged features with wavelet features across all examined non-temporal methods for one-step-forward forecasting, and modest benefit when used as inputs for temporal deep learning-based models for long-horizon forecasting.
|
[
"['Guy P Nason' 'James L. Wei']"
] |
null | null |
2403.08632
| null | null |
http://arxiv.org/pdf/2403.08632v1
|
2024-03-13T15:46:37Z
|
2024-03-13T15:46:37Z
|
A Decade's Battle on Dataset Bias: Are We There Yet?
|
We revisit the "dataset classification" experiment suggested by Torralba and Efros a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets as well as more capable neural network architectures. Surprisingly, we observe that modern neural networks can achieve excellent accuracy in classifying which dataset an image is from: e.g., we report 84.7% accuracy on held-out validation data for the three-way classification problem consisting of the YFCC, CC, and DataComp datasets. Our further experiments show that such a dataset classifier could learn semantic features that are generalizable and transferable, which cannot be simply explained by memorization. We hope our discovery will inspire the community to rethink the issue involving dataset bias and model capabilities.
|
[
"['Zhuang Liu' 'Kaiming He']"
] |
null | null |
2403.08635
| null | null |
http://arxiv.org/pdf/2403.08635v1
|
2024-03-13T15:47:26Z
|
2024-03-13T15:47:26Z
|
Human Alignment of Large Language Models through Online Preference
Optimisation
|
Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensively studied recently and several methods such as Reinforcement Learning from Human Feedback (RLHF), Direct Policy Optimisation (DPO) and Sequence Likelihood Calibration (SLiC) have emerged. In this paper, our contribution is two-fold. First, we show the equivalence between two recent alignment methods, namely Identity Policy Optimisation (IPO) and Nash Mirror Descent (Nash-MD). Second, we introduce a generalisation of IPO, named IPO-MD, that leverages the regularised sampling approach proposed by Nash-MD. This equivalence may seem surprising at first sight, since IPO is an offline method whereas Nash-MD is an online method using a preference model. However, this equivalence can be proven when we consider the online version of IPO, that is when both generations are sampled by the online policy and annotated by a trained preference model. Optimising the IPO loss with such a stream of data becomes then equivalent to finding the Nash equilibrium of the preference model through self-play. Building on this equivalence, we introduce the IPO-MD algorithm that generates data with a mixture policy (between the online and reference policy) similarly as the general Nash-MD algorithm. We compare online-IPO and IPO-MD to different online versions of existing losses on preference data such as DPO and SLiC on a summarisation task.
|
[
"['Daniele Calandriello' 'Daniel Guo' 'Remi Munos' 'Mark Rowland'\n 'Yunhao Tang' 'Bernardo Avila Pires' 'Pierre Harvey Richemond'\n 'Charline Le Lan' 'Michal Valko' 'Tianqi Liu' 'Rishabh Joshi'\n 'Zeyu Zheng' 'Bilal Piot']"
] |
null | null |
2403.08638
| null | null |
http://arxiv.org/pdf/2403.08638v1
|
2024-03-13T15:51:03Z
|
2024-03-13T15:51:03Z
|
Disparate Effect Of Missing Mediators On Transportability of Causal
Effects
|
Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when applied to different populations as a result of factors that mediate the effects. However, when mediators are missing in the population where the effect is to be transported, these estimates could be biased. We study this issue of missing mediators, motivated by challenges in public health, wherein mediators can be missing, not at random. We propose a sensitivity analysis framework that quantifies the impact of missing mediator data on transported mediation effects. This framework enables us to identify the settings under which the conditional transported mediation effect is rendered insignificant for the subgroup with missing mediator data. Specifically, we provide the bounds on the transported mediation effect as a function of missingness. We then apply the framework to longitudinal data from the Moving to Opportunity Study, a large-scale housing voucher experiment, to quantify the effect of missing mediators on transport effect estimates of voucher receipt, an upstream intervention on living location, in childhood on subsequent risk of mental health or substance use disorder mediated through parental health across sites. Our findings provide a tangible understanding of how much missing data can be withstood for unbiased effect estimates.
|
[
"['Vishwali Mhasawade' 'Rumi Chunara']"
] |
null | null |
2403.08652
| null | null |
http://arxiv.org/pdf/2403.08652v1
|
2024-03-13T16:06:26Z
|
2024-03-13T16:06:26Z
|
Extracting Explanations, Justification, and Uncertainty from Black-Box
Deep Neural Networks
|
Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence. In mission critical applications, it is important to both understand associated DNN reasoning and its supporting evidence. In this paper, we propose a novel Bayesian approach to extract explanations, justifications, and uncertainty estimates from DNNs. Our approach is efficient both in terms of memory and computation, and can be applied to any black box DNN without any retraining, including applications to anomaly detection and out-of-distribution detection tasks. We validate our approach on the CIFAR-10 dataset, and show that it can significantly improve the interpretability and reliability of DNNs.
|
[
"['Paul Ardis' 'Arjuna Flenner']"
] |
null | null |
2403.08662
| null | null |
http://arxiv.org/pdf/2403.08662v1
|
2024-03-13T16:16:20Z
|
2024-03-13T16:16:20Z
|
Self-Supervised Learning for Covariance Estimation
|
We consider the use of deep learning for covariance estimation. We propose to globally learn a neural network that will then be applied locally at inference time. Leveraging recent advancements in self-supervised foundational models, we train the network without any labeling by simply masking different samples and learning to predict their covariance given their surrounding neighbors. The architecture is based on the popular attention mechanism. Its main advantage over classical methods is the automatic exploitation of global characteristics without any distributional assumptions or regularization. It can be pre-trained as a foundation model and then be repurposed for various downstream tasks, e.g., adaptive target detection in radar or hyperspectral imagery.
|
[
"['Tzvi Diskin' 'Ami Wiesel']"
] |
null | null |
2403.08664
| null | null |
http://arxiv.org/pdf/2403.08664v2
|
2024-03-14T15:57:59Z
|
2024-03-13T16:17:09Z
|
Zero-shot and Few-shot Generation Strategies for Artificial Clinical
Records
|
The challenge of accessing historical patient data for clinical research, while adhering to privacy regulations, is a significant obstacle in medical science. An innovative approach to circumvent this issue involves utilising synthetic medical records that mirror real patient data without compromising individual privacy. The creation of these synthetic datasets, particularly without using actual patient data to train Large Language Models (LLMs), presents a novel solution as gaining access to sensitive patient information to train models is also a challenge. This study assesses the capability of the Llama 2 LLM to create synthetic medical records that accurately reflect real patient information, employing zero-shot and few-shot prompting strategies for comparison against fine-tuned methodologies that do require sensitive patient data during training. We focus on generating synthetic narratives for the History of Present Illness section, utilising data from the MIMIC-IV dataset for comparison. In this work introduce a novel prompting technique that leverages a chain-of-thought approach, enhancing the model's ability to generate more accurate and contextually relevant medical narratives without prior fine-tuning. Our findings suggest that this chain-of-thought prompted approach allows the zero-shot model to achieve results on par with those of fine-tuned models, based on Rouge metrics evaluation.
|
[
"['Erlend Frayling' 'Jake Lever' 'Graham McDonald']"
] |
null | null |
2403.08673
| null | null |
http://arxiv.org/pdf/2403.08673v1
|
2024-03-13T16:25:55Z
|
2024-03-13T16:25:55Z
|
When can we Approximate Wide Contrastive Models with Neural Tangent
Kernels and Principal Component Analysis?
|
Contrastive learning is a paradigm for learning representations from unlabelled data that has been highly successful for image and text data. Several recent works have examined contrastive losses to claim that contrastive models effectively learn spectral embeddings, while few works show relations between (wide) contrastive models and kernel principal component analysis (PCA). However, it is not known if trained contrastive models indeed correspond to kernel methods or PCA. In this work, we analyze the training dynamics of two-layer contrastive models, with non-linear activation, and answer when these models are close to PCA or kernel methods. It is well known in the supervised setting that neural networks are equivalent to neural tangent kernel (NTK) machines, and that the NTK of infinitely wide networks remains constant during training. We provide the first convergence results of NTK for contrastive losses, and present a nuanced picture: NTK of wide networks remains almost constant for cosine similarity based contrastive losses, but not for losses based on dot product similarity. We further study the training dynamics of contrastive models with orthogonality constraints on output layer, which is implicitly assumed in works relating contrastive learning to spectral embedding. Our deviation bounds suggest that representations learned by contrastive models are close to the principal components of a certain matrix computed from random features. We empirically show that our theoretical results possibly hold beyond two-layer networks.
|
[
"['Gautham Govind Anil' 'Pascal Esser' 'Debarghya Ghoshdastidar']"
] |
null | null |
2403.08687
| null | null |
http://arxiv.org/abs/2403.08687v1
|
2024-03-13T16:44:36Z
|
2024-03-13T16:44:36Z
|
Digital Twin-assisted Reinforcement Learning for Resource-aware
Microservice Offloading in Edge Computing
|
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fundamentally important problem, decides when and where microservices are executed upon the arrival of services. However, the dynamic nature of the real-world CEC environment often leads to inefficient microservice offloading strategies, resulting in underutilized resources and network congestion. To address this challenge, we formulate an online joint microservice offloading and bandwidth allocation problem, JMOBA, to minimize the average completion time of services. In this paper, we introduce a novel microservice offloading algorithm, DTDRLMO, which leverages deep reinforcement learning (DRL) and digital twin technology. Specifically, we employ digital twin techniques to predict and adapt to changing edge node loads and network conditions of CEC in real-time. Furthermore, this approach enables the generation of an efficient offloading plan, selecting the most suitable edge node for each microservice. Simulation results on real-world and synthetic datasets demonstrate that DTDRLMO outperforms heuristic and learning-based methods in average service completion time.
|
[
"['Xiangchun Chen' 'Jiannong Cao' 'Zhixuan Liang' 'Yuvraj Sahni'\n 'Mingjin Zhang']"
] |
null | null |
2403.08699
| null | null |
http://arxiv.org/pdf/2403.08699v1
|
2024-03-13T17:02:27Z
|
2024-03-13T17:02:27Z
|
Implicit Regularization of Gradient Flow on One-Layer Softmax Attention
|
We study gradient flow on the exponential loss for a classification problem with a one-layer softmax attention model, where the key and query weight matrices are trained separately. Under a separability assumption on the data, we show that when gradient flow achieves the minimal loss value, it further implicitly minimizes the nuclear norm of the product of the key and query weight matrices. Such implicit regularization can be described by a Support Vector Machine (SVM) problem with respect to the attention weights. This finding contrasts with prior results showing that the gradient descent induces an implicit regularization on the Frobenius norm on the product weight matrix when the key and query matrices are combined into a single weight matrix for training. For diagonal key and query matrices, our analysis builds upon the reparameterization technique and exploits approximate KKT conditions of the SVM associated with the classification data. Moreover, the results are extended to general weights configurations given proper alignment of the weight matrices' singular spaces with the data features at initialization.
|
[
"['Heejune Sheen' 'Siyu Chen' 'Tianhao Wang' 'Harrison H. Zhou']"
] |
null | null |
2403.08700
| null | null |
http://arxiv.org/pdf/2403.08700v1
|
2024-03-13T17:04:56Z
|
2024-03-13T17:04:56Z
|
Diffusion-based Iterative Counterfactual Explanations for Fetal
Ultrasound Image Quality Assessment
|
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, producing high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or the fetus dynamics. In this work, we propose using diffusion-based counterfactual explainable AI to generate realistic high-quality standard planes from low-quality non-standard ones. Through quantitative and qualitative evaluation, we demonstrate the effectiveness of our method in producing plausible counterfactuals of increased quality. This shows future promise both for enhancing training of clinicians by providing visual feedback, as well as for improving image quality and, consequently, downstream diagnosis and monitoring.
|
[
"['Paraskevas Pegios' 'Manxi Lin' 'Nina Weng'\n 'Morten Bo Søndergaard Svendsen' 'Zahra Bashir' 'Siavash Bigdeli'\n 'Anders Nymark Christensen' 'Martin Tolsgaard' 'Aasa Feragen']"
] |
null | null |
2403.08728
| null | null |
http://arxiv.org/pdf/2403.08728v1
|
2024-03-13T17:28:20Z
|
2024-03-13T17:28:20Z
|
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with
Diffusion Models trained on Corrupted Data
|
We provide a framework for solving inverse problems with diffusion models learned from linearly corrupted data. Our method, Ambient Diffusion Posterior Sampling (A-DPS), leverages a generative model pre-trained on one type of corruption (e.g. image inpainting) to perform posterior sampling conditioned on measurements from a potentially different forward process (e.g. image blurring). We test the efficacy of our approach on standard natural image datasets (CelebA, FFHQ, and AFHQ) and we show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance. We further extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements at various acceleration factors (R=2, 4, 6, 8). We again observe that models trained on highly subsampled data are better priors for solving inverse problems in the high acceleration regime than models trained on fully sampled data. We open-source our code and the trained Ambient Diffusion MRI models: https://github.com/utcsilab/ambient-diffusion-mri .
|
[
"['Asad Aali' 'Giannis Daras' 'Brett Levac' 'Sidharth Kumar'\n 'Alexandros G. Dimakis' 'Jonathan I. Tamir']"
] |
null | null |
2403.08741
| null | null |
http://arxiv.org/pdf/2403.08741v1
|
2024-03-13T17:44:16Z
|
2024-03-13T17:44:16Z
|
Learning How to Strategically Disclose Information
|
Strategic information disclosure, in its simplest form, considers a game between an information provider (sender) who has access to some private information that an information receiver is interested in. While the receiver takes an action that affects the utilities of both players, the sender can design information (or modify beliefs) of the receiver through signal commitment, hence posing a Stackelberg game. However, obtaining a Stackelberg equilibrium for this game traditionally requires the sender to have access to the receiver's objective. In this work, we consider an online version of information design where a sender interacts with a receiver of an unknown type who is adversarially chosen at each round. Restricting attention to Gaussian prior and quadratic costs for the sender and the receiver, we show that $mathcal{O}(sqrt{T})$ regret is achievable with full information feedback, where $T$ is the total number of interactions between the sender and the receiver. Further, we propose a novel parametrization that allows the sender to achieve $mathcal{O}(sqrt{T})$ regret for a general convex utility function. We then consider the Bayesian Persuasion problem with an additional cost term in the objective function, which penalizes signaling policies that are more informative and obtain $mathcal{O}(log(T))$ regret. Finally, we establish a sublinear regret bound for the partial information feedback setting and provide simulations to support our theoretical results.
|
[
"['Raj Kiriti Velicheti' 'Melih Bastopcu' 'S. Rasoul Etesami' 'Tamer Başar']"
] |
null | null |
2403.08743
| null | null |
http://arxiv.org/pdf/2403.08743v1
|
2024-03-13T17:46:28Z
|
2024-03-13T17:46:28Z
|
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing
Framework
|
Large language models (LLMs) can easily generate biased and discriminative responses. As LLMs tap into consequential decision-making (e.g., hiring and healthcare), it is of crucial importance to develop strategies to mitigate these biases. This paper focuses on social bias, tackling the association between demographic information and LLM outputs. We propose a causality-guided debiasing framework that utilizes causal understandings of (1) the data-generating process of the training corpus fed to LLMs, and (2) the internal reasoning process of LLM inference, to guide the design of prompts for debiasing LLM outputs through selection mechanisms. Our framework unifies existing de-biasing prompting approaches such as inhibitive instructions and in-context contrastive examples, and sheds light on new ways of debiasing by encouraging bias-free reasoning. Our strong empirical performance on real-world datasets demonstrates that our framework provides principled guidelines on debiasing LLM outputs even with only the black-box access.
|
[
"['Jingling Li' 'Zeyu Tang' 'Xiaoyu Liu' 'Peter Spirtes' 'Kun Zhang'\n 'Liu Leqi' 'Yang Liu']"
] |
null | null |
2403.08750
| null | null |
http://arxiv.org/pdf/2403.08750v1
|
2024-03-13T17:51:02Z
|
2024-03-13T17:51:02Z
|
Neural reproducing kernel Banach spaces and representer theorems for
deep networks
|
Studying the function spaces defined by neural networks helps to understand the corresponding learning models and their inductive bias. While in some limits neural networks correspond to function spaces that are reproducing kernel Hilbert spaces, these regimes do not capture the properties of the networks used in practice. In contrast, in this paper we show that deep neural networks define suitable reproducing kernel Banach spaces. These spaces are equipped with norms that enforce a form of sparsity, enabling them to adapt to potential latent structures within the input data and their representations. In particular, leveraging the theory of reproducing kernel Banach spaces, combined with variational results, we derive representer theorems that justify the finite architectures commonly employed in applications. Our study extends analogous results for shallow networks and can be seen as a step towards considering more practically plausible neural architectures.
|
[
"['Francesca Bartolucci' 'Ernesto De Vito' 'Lorenzo Rosasco'\n 'Stefano Vigogna']"
] |
null | null |
2403.08755
| null | null |
http://arxiv.org/pdf/2403.08755v2
|
2024-04-22T19:17:49Z
|
2024-03-13T17:53:47Z
|
DAM: Dynamic Adapter Merging for Continual Video QA Learning
|
We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses the proposed Dynamic Adapter Merging to (i) mitigate catastrophic forgetting, (ii) enable efficient adaptation to continually arriving datasets, (iii) handle inputs from unknown datasets during inference, and (iv) enable knowledge sharing across similar dataset domains. Given a set of continually streaming VidQA datasets, we sequentially train dataset-specific adapters for each dataset while freezing the parameters of a large pretrained video-language backbone. During inference, given a video-question sample from an unknown domain, our method first uses the proposed non-parametric router function to compute a probability for each adapter, reflecting how relevant that adapter is to the current video-question input instance. Subsequently, the proposed dynamic adapter merging scheme aggregates all the adapter weights into a new adapter instance tailored for that particular test sample to compute the final VidQA prediction, mitigating the impact of inaccurate router predictions and facilitating knowledge sharing across domains. Our DAM model outperforms prior state-of-the-art continual learning approaches by 9.1% while exhibiting 1.9% less forgetting on 6 VidQA datasets spanning various domains. We further extend DAM to continual image classification and image QA and outperform prior methods by a large margin. The code is publicly available at: https://github.com/klauscc/DAM
|
[
"['Feng Cheng' 'Ziyang Wang' 'Yi-Lin Sung' 'Yan-Bo Lin' 'Mohit Bansal'\n 'Gedas Bertasius']"
] |
null | null |
2403.08757
| null | null |
http://arxiv.org/pdf/2403.08757v2
|
2024-03-14T16:40:51Z
|
2024-03-13T17:55:34Z
|
Efficient Combinatorial Optimization via Heat Diffusion
|
Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature.The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal. To overcome this challenge, diverging from conventional efforts of expanding the solver's search scope, we focus on enabling information to actively propagate to the solver through heat diffusion. By transforming the target function while preserving its optima, heat diffusion facilitates information flow from distant regions to the solver, providing more efficient navigation. Utilizing heat diffusion, we propose a framework for solving general combinatorial optimization problems. The proposed methodology demonstrates superior performance across a range of the most challenging and widely encountered combinatorial optimizations. Echoing recent advancements in harnessing thermodynamics for generative artificial intelligence, our study further reveals its significant potential in advancing combinatorial optimization.
|
[
"['Hengyuan Ma' 'Wenlian Lu' 'Jianfeng Feng']"
] |
null | null |
2403.08763
| null | null |
http://arxiv.org/pdf/2403.08763v3
|
2024-03-26T17:58:48Z
|
2024-03-13T17:58:57Z
|
Simple and Scalable Strategies to Continually Pre-train Large Language
Models
|
Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes available. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by the final loss and the average score on several language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (English$rightarrow$English) and a stronger distribution shift (English$rightarrow$German) at the $405$M parameter model scale with large dataset sizes (hundreds of billions of tokens). Selecting the weak but realistic shift for larger-scale experiments, we also find that our continual learning strategies match the re-training baseline for a 10B parameter LLM. Our results demonstrate that LLMs can be successfully updated via simple and scalable continual learning strategies, matching the re-training baseline using only a fraction of the compute. Finally, inspired by previous work, we propose alternatives to the cosine learning rate schedule that help circumvent forgetting induced by LR re-warming and that are not bound to a fixed token budget.
|
[
"['Adam Ibrahim' 'Benjamin Thérien' 'Kshitij Gupta' 'Mats L. Richter'\n 'Quentin Anthony' 'Timothée Lesort' 'Eugene Belilovsky' 'Irina Rish']"
] |
null | null |
2403.08787
| null | null |
http://arxiv.org/pdf/2403.08787v1
|
2024-01-30T02:03:18Z
|
2024-01-30T02:03:18Z
|
Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
|
Multiview subspace clustering (MVSC) has attracted an increasing amount of attention in recent years. Most existing MVSC methods first collect complementary information from different views and consequently derive a consensus reconstruction coefficient matrix to indicate the subspace structure of a multi-view data set. In this paper, we initially assume the existence of a consensus reconstruction coefficient matrix and then use it to build a consensus graph filter. In each view, the filter is employed for smoothing the data and designing a regularizer for the reconstruction coefficient matrix. Finally, the obtained reconstruction coefficient matrices from different views are used to create constraints for the consensus reconstruction coefficient matrix. Therefore, in the proposed method, the consensus reconstruction coefficient matrix, the consensus graph filter, and the reconstruction coefficient matrices from different views are interdependent. We provide an optimization algorithm to obtain their optimal values. Extensive experiments on diverse multi-view data sets demonstrate that our approach outperforms some state-of-the-art methods.
|
[
"['Lai Wei' 'Shanshan Song']"
] |
null | null |
2403.08789
| null | null |
http://arxiv.org/pdf/2403.08789v1
|
2024-01-30T09:13:49Z
|
2024-01-30T09:13:49Z
|
Bridging Human Concepts and Computer Vision for Explainable Face
Verification
|
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation's interpretability of a face verification algorithm. In particular, we are inspired by the human perceptual process to understand how machines perceive face's human-semantic areas during face comparison tasks. We use Mediapipe, which provides a segmentation technique that identifies distinct human-semantic facial regions, enabling the machine's perception analysis. Additionally, we adapted two model-agnostic algorithms to provide human-interpretable insights into the decision-making processes.
|
[
"['Miriam Doh' 'Caroline Mazini Rodrigues' 'Nicolas Boutry'\n 'Laurent Najman' 'Matei Mancas' 'Hugues Bersini']"
] |
null | null |
2403.08792
| null | null |
http://arxiv.org/pdf/2403.08792v1
|
2024-01-30T16:12:20Z
|
2024-01-30T16:12:20Z
|
Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge
AI Accelerators
|
The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics. We investigate two hardware options for the deployment of FER machine learning (ML) models at the edge: neuromorphic hardware versus edge AI accelerators. Our study includes exhaustive experiments providing comparative analyses between the Intel Loihi neuromorphic processor and four distinct edge platforms: Raspberry Pi-4, Intel Neural Compute Stick (NSC), Jetson Nano, and Coral TPU. The results obtained show that Loihi can achieve approximately two orders of magnitude reduction in power dissipation and one order of magnitude energy savings compared to Coral TPU which happens to be the least power-intensive and energy-consuming edge AI accelerator. These reductions in power and energy are achieved while the neuromorphic solution maintains a comparable level of accuracy with the edge accelerators, all within the real-time latency requirements.
|
[
"['Heath Smith' 'James Seekings' 'Mohammadreza Mohammadi' 'Ramtin Zand']"
] |
null | null |
2403.08793
| null | null |
http://arxiv.org/pdf/2403.08793v1
|
2024-01-30T17:21:28Z
|
2024-01-30T17:21:28Z
|
Neural Loss Function Evolution for Large-Scale Image Classifier
Convolutional Neural Networks
|
For classification, neural networks typically learn by minimizing cross-entropy, but are evaluated and compared using accuracy. This disparity suggests neural loss function search (NLFS), the search for a drop-in replacement loss function of cross-entropy for neural networks. We apply NLFS to image classifier convolutional neural networks. We propose a new search space for NLFS that encourages more diverse loss functions to be explored, and a surrogate function that accurately transfers to large-scale convolutional neural networks. We search the space using regularized evolution, a mutation-only aging genetic algorithm. After evolution and a proposed loss function elimination protocol, we transferred the final loss functions across multiple architectures, datasets, and image augmentation techniques to assess generalization. In the end, we discovered three new loss functions, called NeuroLoss1, NeuroLoss2, and NeuroLoss3 that were able to outperform cross-entropy in terms of a higher mean test accuracy as a simple drop-in replacement loss function across the majority of experiments.
|
[
"['Brandon Morgan' 'Dean Hougen']"
] |
null | null |
2403.08802
| null | null |
http://arxiv.org/pdf/2403.08802v2
|
2024-06-09T19:48:05Z
|
2024-02-05T14:20:19Z
|
Governance of Generative Artificial Intelligence for Companies
|
Generative Artificial Intelligence (GenAI), specifically large language models like ChatGPT, has swiftly entered organizations without adequate governance, posing both opportunities and risks. Despite extensive debates on GenAI's transformative nature and regulatory measures, limited research addresses organizational governance, encompassing technical and business perspectives. Our review paper fills this gap by surveying recent works with the purpose of developing a framework for GenAI governance within companies. This framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities as well as mitigate risks associated with GenAI integration. Our research contributes a focused approach to GenAI governance, offering practical insights for companies navigating the challenges of GenAI adoption and highlighting research gaps.
|
[
"['Johannes Schneider' 'Rene Abraham' 'Christian Meske']"
] |
null | null |
2403.08804
| null | null |
http://arxiv.org/pdf/2403.08804v1
|
2024-02-06T09:07:12Z
|
2024-02-06T09:07:12Z
|
Forward Direct Feedback Alignment for Online Gradient Estimates of
Spiking Neural Networks
|
There is an interest in finding energy efficient alternatives to current state of the art neural network training algorithms. Spiking neural network are a promising approach, because they can be simulated energy efficiently on neuromorphic hardware platforms. However, these platforms come with limitations on the design of the training algorithm. Most importantly, backpropagation cannot be implemented on those. We propose a novel neuromorphic algorithm, the textit{Spiking Forward Direct Feedback Alignment} (SFDFA) algorithm, an adaption of textit{Forward Direct Feedback Alignment} to train SNNs. SFDFA estimates the weights between output and hidden neurons as feedback connections. The main contribution of this paper is to describe how exact local gradients of spikes can be computed in an online manner while taking into account the intra-neuron dependencies between post-synaptic spikes and derive a dynamical system for neuromorphic hardware compatibility. We compare the SFDFA algorithm with a number of competitor algorithms and show that the proposed algorithm achieves higher performance and convergence rates.
|
[
"['Florian Bacho' 'Dminique Chu']"
] |
null | null |
2403.08806
| null | null |
http://arxiv.org/abs/2403.08806v1
|
2024-02-06T11:35:05Z
|
2024-02-06T11:35:05Z
|
Adversarially Robust Deepfake Detection via Adversarial Feature
Similarity Learning
|
Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can deceive the detection models into producing incorrect outputs. To tackle this critical issue, we introduce Adversarial Feature Similarity Learning (AFSL), which integrates three fundamental deep feature learning paradigms. By optimizing the similarity between samples and weight vectors, our approach aims to distinguish between real and fake instances. Additionally, we aim to maximize the similarity between both adversarially perturbed examples and unperturbed examples, regardless of their real or fake nature. Moreover, we introduce a regularization technique that maximizes the dissimilarity between real and fake samples, ensuring a clear separation between these two categories. With extensive experiments on popular deepfake datasets, including FaceForensics++, FaceShifter, and DeeperForensics, the proposed method outperforms other standard adversarial training-based defense methods significantly. This further demonstrates the effectiveness of our approach to protecting deepfake detectors from adversarial attacks.
|
[
"['Sarwar Khan']"
] |
null | null |
2403.08810
| null | null |
http://arxiv.org/abs/2403.08810v1
|
2024-02-07T21:15:18Z
|
2024-02-07T21:15:18Z
|
Comparison of edge computing methods in Internet of Things architectures
for efficient estimation of indoor environmental parameters with Machine
Learning
|
The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35 to 76% is accomplished with an adequate balance between performance and energy efficiency.
|
[
"['Jose-Carlos Gamazo-Real' 'Raul Torres Fernandez' 'Adrian Murillo Armas']"
] |
null | null |
2403.08812
| null | null |
http://arxiv.org/pdf/2403.08812v1
|
2024-02-09T18:16:17Z
|
2024-02-09T18:16:17Z
|
Gore Diffusion LoRA Model
|
The Emergence of Artificial Intelligence (AI) has significantly impacted our engagement with violence, sparking ethical deliberations regarding the algorithmic creation of violent imagery. This paper scrutinizes the "Gore Diffusion LoRA Model," an innovative AI model proficient in generating hyper-realistic visuals portraying intense violence and bloodshed. Our exploration encompasses the model's technical intricacies, plausible applications, and the ethical quandaries inherent in its utilization. We contend that the creation and implementation of such models warrant a meticulous discourse concerning the convergence of AI, art, and violence. Furthermore, we advocate for a structured framework advocating responsible development and ethical deployment of these potent technologies.
|
[
"['Ayush Thakur' 'Ashwani Kumar Dubey']"
] |
null | null |
2403.08813
| null | null |
http://arxiv.org/pdf/2403.08813v1
|
2024-02-10T10:34:20Z
|
2024-02-10T10:34:20Z
|
Federated Deep Q-Learning and 5G load balancing
|
Despite advances in cellular network technology, base station (BS) load balancing remains a persistent problem. Although centralized resource allocation methods can address the load balancing problem, it still remains an NP-hard problem. In this research, we study how federated deep Q learning can be used to inform each user equipment (UE) of the each BS's load conditions. Federated deep Q learning's load balancing enables intelligent UEs to independently select the best BS while also limiting the amount of private information exposed to the network. In this study, we propose and analyze a federated deep Q learning load balancing system, which is implemented using the Open-RAN xAPP framework and the near-Real Time Radio Interface Controller (near-RT RIC). Our simulation results indicate that compared to the maximum Signal-To-Noise-Ratio (MAX-SINR) method currently used by UEs, our proposed deep Q learning model can consistently provide better High average UE quality of service
|
[
"['Hsin Lin' 'Yi-Kang Su' 'Hong-Qi Chen' 'La-Fei Ko']"
] |
null | null |
2403.08818
| null | null |
http://arxiv.org/pdf/2403.08818v1
|
2024-02-19T23:48:40Z
|
2024-02-19T23:48:40Z
|
Multimodal Fusion of EHR in Structures and Semantics: Integrating
Clinical Records and Notes with Hypergraph and LLM
|
Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete picture of the health status of a patient. While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied. This is mostly because of the complex medical coding systems used and the noise and redundancy present in the written notes. In this work, we propose a new framework called MINGLE, which integrates both structures and semantics in EHR effectively. Our framework uses a two-level infusion strategy to combine medical concept semantics and clinical note semantics into hypergraph neural networks, which learn the complex interactions between different types of data to generate visit representations for downstream prediction. Experiment results on two EHR datasets, the public MIMIC-III and private CRADLE, show that MINGLE can effectively improve predictive performance by 11.83% relatively, enhancing semantic integration as well as multimodal fusion for structural and textual EHR data.
|
[
"['Hejie Cui' 'Xinyu Fang' 'Ran Xu' 'Xuan Kan' 'Joyce C. Ho' 'Carl Yang']"
] |
null | null |
2403.08819
| null | null |
http://arxiv.org/pdf/2403.08819v2
|
2024-06-27T16:30:32Z
|
2024-02-20T04:13:48Z
|
Thermometer: Towards Universal Calibration for Large Language Models
|
We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method.
|
[
"['Maohao Shen' 'Subhro Das' 'Kristjan Greenewald' 'Prasanna Sattigeri'\n 'Gregory Wornell' 'Soumya Ghosh']"
] |
null | null |
2403.08820
| null | null |
http://arxiv.org/pdf/2403.08820v1
|
2024-02-21T19:36:24Z
|
2024-02-21T19:36:24Z
|
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with
Interpretable Dietary Patterns
|
The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the various side effects can trigger opioid relapse. In addition to MAT, the dietary nutrition intervention has been demonstrated its importance in opioid misuse prevention and recovery. However, research on the alarming connections between dietary patterns and opioid misuse remain under-explored. In response to this gap, in this paper, we first establish a large-scale multifaceted dietary benchmark dataset related to opioid users at the first attempt and then develop a novel framework - i.e., namely Opioid Misuse Detection with Interpretable Dietary Patterns (Diet-ODIN) - to bridge heterogeneous graph (HG) and large language model (LLM) for the identification of users with opioid misuse and the interpretation of their associated dietary patterns. Specifically, in Diet-ODIN, we first construct an HG to comprehensively incorporate both dietary and health-related information, and then we devise a holistic graph learning framework with noise reduction to fully capitalize both users' individual dietary habits and shared dietary patterns for the detection of users with opioid misuse. To further delve into the intricate correlations between dietary patterns and opioid misuse, we exploit an LLM by utilizing the knowledge obtained from the graph learning model for interpretation. The extensive experimental results based on our established benchmark with quantitative and qualitative measures demonstrate the outstanding performance of Diet-ODIN in exploring the complex interplay between opioid misuse and dietary patterns, by comparison with state-of-the-art baseline methods.
|
[
"['Zheyuan Zhang' 'Zehong Wang' 'Shifu Hou' 'Evan Hall' 'Landon Bachman'\n 'Vincent Galassi' 'Jasmine White' 'Nitesh V. Chawla' 'Chuxu Zhang'\n 'Yanfang Ye']"
] |
null | null |
2403.08821
| null | null |
http://arxiv.org/pdf/2403.08821v1
|
2024-02-24T05:48:05Z
|
2024-02-24T05:48:05Z
|
Effective Gradient Sample Size via Variation Estimation for Accelerating
Sharpness aware Minimization
|
Sharpness-aware Minimization (SAM) has been proposed recently to improve model generalization ability. However, SAM calculates the gradient twice in each optimization step, thereby doubling the computation costs compared to stochastic gradient descent (SGD). In this paper, we propose a simple yet efficient sampling method to significantly accelerate SAM. Concretely, we discover that the gradient of SAM is a combination of the gradient of SGD and the Projection of the Second-order gradient matrix onto the First-order gradient (PSF). PSF exhibits a gradually increasing frequency of change during the training process. To leverage this observation, we propose an adaptive sampling method based on the variation of PSF, and we reuse the sampled PSF for non-sampling iterations. Extensive empirical results illustrate that the proposed method achieved state-of-the-art accuracies comparable to SAM on diverse network architectures.
|
[
"['Jiaxin Deng' 'Junbiao Pang' 'Baochang Zhang' 'Tian Wang']"
] |
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