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2403.08140 | BAGEL: Bootstrapping Agents by Guiding Exploration with Language | Following natural language instructions by executing actions in digital environments (e.g. web-browsers and REST APIs) is a challenging task for language model (LM) agents. Unfortunately, LM agents often fail to generalize to new environments without human demonstrations. This work presents BAGEL, a method for bootstrapping LM agents without human supervision. BAGEL converts a seed set of randomly explored trajectories or synthetic instructions, into demonstrations, via round-trips between two noisy LM components: an LM labeler which converts a trajectory into a synthetic instruction, and a zero-shot LM agent which maps the synthetic instruction into a refined trajectory. By performing these round-trips iteratively, BAGEL quickly converts the initial distribution of trajectories towards those that are well-described by natural language. We use BAGEL demonstrations to adapt a zero shot LM agent at test time via in-context learning over retrieved demonstrations, and find improvements of over 2-13% absolute on ToolQA and MiniWob++, with up to 13x reduction in execution failures. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 437,189 |
2008.05286 | Secure IoT Data Analytics in Cloud via Intel SGX | The growing adoption of IoT devices in our daily life is engendering a data deluge, mostly private information that needs careful maintenance and secure storage system to ensure data integrity and protection. Also, the prodigious IoT ecosystem has provided users with opportunities to automate systems by interconnecting their devices and other services with rule-based programs. The cloud services that are used to store and process sensitive IoT data turn out to be vulnerable to outside threats. Hence, sensitive IoT data and rule-based programs need to be protected against cyberattacks. To address this important challenge, in this paper, we propose a framework to maintain confidentiality and integrity of IoT data and rule-based program execution. We design the framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, and end-to-end data encryption mechanism. We evaluate the framework by executing rule-based programs in the SGX securely with both simulated and real IoT device data. | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | false | 191,474 |
2403.02951 | Benchmarking the Text-to-SQL Capability of Large Language Models: A
Comprehensive Evaluation | Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods. Nevertheless, as a nascent research field, there is still no consensus on the optimal prompt templates and design frameworks. Additionally, existing benchmarks inadequately explore the performance of LLMs across the various sub-tasks of the Text-to-SQL process, which hinders the assessment of LLMs' cognitive capabilities and the optimization of LLM-based solutions. To address the aforementioned issues, we firstly construct a new dataset designed to mitigate the risk of overfitting in LLMs. Then we formulate five evaluation tasks to comprehensively assess the performance of diverse methods across various LLMs throughout the Text-to-SQL process.Our study highlights the performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task. These findings offer valuable insights for enhancing the development of LLM-based Text-to-SQL systems. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 435,008 |
2407.07054 | A Differentially Private Blockchain-Based Approach for Vertical
Federated Learning | We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applications. DP-BBVFL uses a smart contract to aggregate the feature representations, i.e., the embeddings, from clients transparently. We apply local differential privacy to provide privacy for embeddings stored on a blockchain, hence protecting the original data. We provide the first prototype application of differential privacy with blockchain for vertical federated learning. Our experiments with medical data show that DP-BBVFL achieves high accuracy with a tradeoff in training time due to on-chain aggregation. This innovative fusion of differential privacy and blockchain technology in DP-BBVFL could herald a new era of collaborative and trustworthy machine learning applications across several decentralized application domains. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | true | 471,625 |
2303.03169 | A Unified Algebraic Perspective on Lipschitz Neural Networks | Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant. The goal is to increase and sometimes guarantee the robustness against adversarial attacks. Recent promising techniques draw inspirations from different backgrounds to design 1-Lipschitz neural networks, just to name a few: convex potential layers derive from the discretization of continuous dynamical systems, Almost-Orthogonal-Layer proposes a tailored method for matrix rescaling. However, it is today important to consider the recent and promising contributions in the field under a common theoretical lens to better design new and improved layers. This paper introduces a novel algebraic perspective unifying various types of 1-Lipschitz neural networks, including the ones previously mentioned, along with methods based on orthogonality and spectral methods. Interestingly, we show that many existing techniques can be derived and generalized via finding analytical solutions of a common semidefinite programming (SDP) condition. We also prove that AOL biases the scaled weight to the ones which are close to the set of orthogonal matrices in a certain mathematical manner. Moreover, our algebraic condition, combined with the Gershgorin circle theorem, readily leads to new and diverse parameterizations for 1-Lipschitz network layers. Our approach, called SDP-based Lipschitz Layers (SLL), allows us to design non-trivial yet efficient generalization of convex potential layers. Finally, the comprehensive set of experiments on image classification shows that SLLs outperform previous approaches on certified robust accuracy. Code is available at https://github.com/araujoalexandre/Lipschitz-SLL-Networks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 349,627 |
2409.06708 | Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI
Systems | With the rapid advancement of AI, there is a growing trend to integrate AI into decision-making processes. However, AI systems may exhibit biases that lead decision-makers to draw unfair conclusions. Notably, the COMPAS system used in the American justice system to evaluate recidivism was found to favor racial majority groups; specifically, it violates a fairness standard called equalized odds. Various measures have been proposed to assess AI fairness. We present a framework for auditing AI fairness, involving third-party auditors and AI system providers, and we have created a tool to facilitate systematic examination of AI systems. The tool is open-sourced and publicly available. Unlike traditional AI systems, we advocate a transparent white-box and statistics-based approach. It can be utilized by third-party auditors, AI developers, or the general public for reference when judging the fairness criterion of AI systems. | true | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | false | false | 487,236 |
2405.00680 | Comparative approach: Electric distribution optimization with loss
minimization algorithm and particle swarm optimization | Power systems are very large and complex, it can be influenced by many unexpected events this makes power system optimization problems difficult to solve, hence methods for solving these problems ought to be an active research topic. This review presents an overview of important mathematical comparaison of loss minimization algorithm and particle swarm optimization algorithm in terms of the performances of electric distribution. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 451,017 |
2006.07953 | Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative
Priors | Many problems in statistics and machine learning require the reconstruction of a rank-one signal matrix from noisy data. Enforcing additional prior information on the rank-one component is often key to guaranteeing good recovery performance. One such prior on the low-rank component is sparsity, giving rise to the sparse principal component analysis problem. Unfortunately, there is strong evidence that this problem suffers from a computational-to-statistical gap, which may be fundamental. In this work, we study an alternative prior where the low-rank component is in the range of a trained generative network. We provide a non-asymptotic analysis with optimal sample complexity, up to logarithmic factors, for rank-one matrix recovery under an expansive-Gaussian network prior. Specifically, we establish a favorable global optimization landscape for a nonlinear least squares objective, provided the number of samples is on the order of the dimensionality of the input to the generative model. This result suggests that generative priors have no computational-to-statistical gap for structured rank-one matrix recovery in the finite data, nonasymptotic regime. We present this analysis in the case of both the Wishart and Wigner spiked matrix models. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | 182,013 |
2110.13058 | Some like it tough: Improving model generalization via progressively
increasing the training difficulty | In this work, we propose to progressively increase the training difficulty during learning a neural network model via a novel strategy which we call mini-batch trimming. This strategy makes sure that the optimizer puts its focus in the later training stages on the more difficult samples, which we identify as the ones with the highest loss in the current mini-batch. The strategy is very easy to integrate into an existing training pipeline and does not necessitate a change of the network model. Experiments on several image classification problems show that mini-batch trimming is able to increase the generalization ability (measured via final test error) of the trained model. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 263,057 |
1708.03999 | ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural
Networks without Training Substitute Models | Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack and significantly outperforms existing black-box attacks via substitute models. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 78,867 |
2104.08478 | Sentence Concatenation Approach to Data Augmentation for Neural Machine
Translation | Neural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy. However, it shows poor performance in the translation of long sentences, which is a major issue in low-resource languages. It is assumed that this issue is caused by insufficient number of long sentences in the training data. Therefore, this study proposes a simple data augmentation method to handle long sentences. In this method, we use only the given parallel corpora as the training data and generate long sentences by concatenating two sentences. Based on the experimental results, we confirm improvements in long sentence translation by the proposed data augmentation method, despite its simplicity. Moreover, the translation quality is further improved by the proposed method, when combined with back-translation. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 230,819 |
2502.10367 | Decentralized State Estimation and Opacity Verification Based on
Partially Ordered Observation Sequences | In this paper, we investigate state estimation and opacity verification problems within a decentralized observation architecture. Specifically, we consider a discrete event system whose behavior is recorded by a set of observation sites. These sites transmit the partially ordered sequences of observations that they record to a coordinator whenever a \textit{synchronization} occurs. To properly analyze the system behavior from the coordinator's viewpoint, we first introduce the notion of an \textit{All Sequence Structure} (ASS), which concisely captures the state evolution of each system state upon different information provided by the observation sites. Based on the ASS, we then construct corresponding current-state and initial-state estimators for offline state estimation at the coordinator. When used to verify state-isolation properties under this decentralized architecture, the use of ASS demonstrates a significant reduction in complexity compared with existing approaches in the literature. In particular, we discuss how to verify initial-state opacity at the coordinator, as well as a novel opacity notion, namely current-state-at-synchronization opacity. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 533,834 |
1708.01729 | Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x))
Alternative | In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. --- An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". Please refer Section 6 in 1703.02000 for detailed analysis on Inception Score, and refer its appendix for the discussions on Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative. | false | false | false | false | true | false | true | false | false | false | false | true | false | false | false | false | false | false | 78,438 |
1910.00411 | Generating Fair Universal Representations using Adversarial Models | We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our framework leverages recent advances in adversarial learning to allow a data holder to learn representations in which a set of sensitive attributes are decoupled from the rest of the dataset. We formulate this as a constrained minimax game between an encoder and an adversary where the constraint ensures a measure of usefulness (utility) of the representation. The resulting problem is that of censoring, i.e., finding a representation that is least informative about the sensitive attributes given a utility constraint. For appropriately chosen adversarial loss functions, our censoring framework precisely clarifies the optimal adversarial strategy against strong information-theoretic adversaries; it also achieves the fairness measure of demographic parity for the resulting constrained representations. We evaluate the performance of our proposed framework on both synthetic and publicly available datasets. For these datasets, we use two tradeoff measures: censoring vs. representation fidelity and fairness vs. utility for downstream tasks, to amply demonstrate that multiple sensitive features can be effectively censored even as the resulting fair representations ensure accuracy for multiple downstream tasks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 147,656 |
1910.04235 | Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual
Algorithm for High-Dimensional Data Mining | Recently, reducing communication time between machines becomes the main focus of distributed data mining. Previous methods propose to make workers do more computation locally before aggregating local solutions in the server such that fewer communication rounds between server and workers are required. However, these methods do not consider reducing the communication time per round and work very poor under certain conditions, for example, when there are straggler problems or the dataset is of high dimension. In this paper, we target to reduce communication time per round as well as the required communication rounds. We propose a communication-efficient distributed primal-dual method with straggler-agnostic server and bandwidth-efficient workers. We analyze the convergence property and prove that the proposed method guarantees linear convergence rate to the optimal solution for convex problems. Finally, we conduct large-scale experiments in simulated and real distributed systems and experimental results demonstrate that the proposed method is much faster than compared methods. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 148,706 |
2404.00633 | IPT-V2: Efficient Image Processing Transformer using Hierarchical
Attentions | Recent advances have demonstrated the powerful capability of transformer architecture in image restoration. However, our analysis indicates that existing transformerbased methods can not establish both exact global and local dependencies simultaneously, which are much critical to restore the details and missing content of degraded images. To this end, we present an efficient image processing transformer architecture with hierarchical attentions, called IPTV2, adopting a focal context self-attention (FCSA) and a global grid self-attention (GGSA) to obtain adequate token interactions in local and global receptive fields. Specifically, FCSA applies the shifted window mechanism into the channel self-attention, helps capture the local context and mutual interaction across channels. And GGSA constructs long-range dependencies in the cross-window grid, aggregates global information in spatial dimension. Moreover, we introduce structural re-parameterization technique to feed-forward network to further improve the model capability. Extensive experiments demonstrate that our proposed IPT-V2 achieves state-of-the-art results on various image processing tasks, covering denoising, deblurring, deraining and obtains much better trade-off for performance and computational complexity than previous methods. Besides, we extend our method to image generation as latent diffusion backbone, and significantly outperforms DiTs. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 443,037 |
1903.11246 | Topological Controllability of Undirected Networks of
Diffusively-Coupled Agents | This paper presents conditions for establishing topological controllability in undirected networks of diffusively coupled agents. Specifically, controllability is considered based on the signs of the edges (negative, positive or zero). Our approach differs from well-known structural controllability conditions for linear systems or consensus networks, where controllability conditions are based on edge connectivity (i.e., zero or nonzero edges). Our results first provide a process for merging controllable graphs into a larger controllable graph. Then, based on this process, we provide a graph decomposition process for evaluating the topological controllability of a given network. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 125,463 |
2003.14159 | Detecting impending malnutrition of elderly people in domestic smart
home environments | Proper nutrition is very important for the well-being and independence of elderly people. A significant loss of body weight or a decrease of the Body Mass Index respectively is an indicator for malnutrition. A continuous monitoring of the BMI enables doctors and nutritionists to intervene on impending malnutrition. However, continuous monitoring of the BMI by professionals is not applicable and self-monitoring not reliable. In this article a method for monitoring the trend of the BMI based on ambient sensors is introduced. The ambient sensors are used to measure the time a person spends for preparing meals at home. When the trend of the average time for 4 weeks changes, so does the trend of the BMI for those 4 weeks. Both values show a very strong correlation. Thus, the average time for preparing a meal is a suitable indicator for doctors and nutritionists to examine the patient further, become aware of an impending malnutrition, and intervene at an early stage of malnutrition. The method has been tested on a real-world dataset collected during a 10-month field study with 20 participants of an age of about 85 years. | false | false | false | false | false | true | false | false | false | false | false | false | false | true | false | false | false | false | 170,430 |
1910.05789 | On the Utility of Learning about Humans for Human-AI Coordination | While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Agents that assume their partner to be optimal or similar to them can converge to coordination protocols that fail to understand and be understood by humans. To demonstrate this, we introduce a simple environment that requires challenging coordination, based on the popular game Overcooked, and learn a simple model that mimics human play. We evaluate the performance of agents trained via self-play and population-based training. These agents perform very well when paired with themselves, but when paired with our human model, they are significantly worse than agents designed to play with the human model. An experiment with a planning algorithm yields the same conclusion, though only when the human-aware planner is given the exact human model that it is playing with. A user study with real humans shows this pattern as well, though less strongly. Qualitatively, we find that the gains come from having the agent adapt to the human's gameplay. Given this result, we suggest several approaches for designing agents that learn about humans in order to better coordinate with them. Code is available at https://github.com/HumanCompatibleAI/overcooked_ai. | true | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 149,171 |
2305.00244 | A Critical Analysis of the Limitation of Deep Learning based 3D Dental
Mesh Segmentation Methods in Segmenting Partial Scans | Tooth segmentation from intraoral scans is a crucial part of digital dentistry. Many Deep Learning based tooth segmentation algorithms have been developed for this task. In most of the cases, high accuracy has been achieved, although, most of the available tooth segmentation techniques make an implicit restrictive assumption of full jaw model and they report accuracy based on full jaw models. Medically, however, in certain cases, full jaw tooth scan is not required or may not be available. Given this practical issue, it is important to understand the robustness of currently available widely used Deep Learning based tooth segmentation techniques. For this purpose, we applied available segmentation techniques on partial intraoral scans and we discovered that the available deep Learning techniques under-perform drastically. The analysis and comparison presented in this work would help us in understanding the severity of the problem and allow us to develop robust tooth segmentation technique without strong assumption of full jaw model. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 361,266 |
1902.07781 | Empathic Autonomous Agents | Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We introduce a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) by considering their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable. To illustrate how empathic autonomous agents work, we provide running examples and a simple prototype implementation in a general-purpose programing language. To give a high-level overview of our work, we propose a reasoning-loop architecture for our empathic agent. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | 122,055 |
1812.08947 | Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural
Network Approach | The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end to end Ability aware Person Job Fit Neural Network model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word level semantic representation for both job requirements and job seekers' experiences based on Recurrent Neural Network. Along this line, four hierarchical ability aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large scale real world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 117,069 |
2402.06075 | Scaling Artificial Intelligence for Digital Wargaming in Support of
Decision-Making | In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making. By advancing AI-enabled systems and pairing these with human judgment, we will be able to enhance all-domain awareness, improve the speed and quality of our decision cycles, offer recommendations for novel courses of action, and more rapidly counter our adversary's actions. It therefore becomes imperative that we accelerate the development of AI to help us better address the complexity of modern challenges and dilemmas that currently requires human intelligence and, if possible, attempt to surpass human intelligence--not to replace humans, but to augment and better inform human decision-making at machine speed. Although deep reinforcement learning continues to show promising results in intelligent agent behavior development for the long-horizon, complex tasks typically found in combat modeling and simulation, further research is needed to enable the scaling of AI to deal with these intricate and expansive state-spaces characteristic of wargaming for either concept development, education, or analysis. To help address this challenge, in our research, we are developing and implementing a hierarchical reinforcement learning framework that includes a multi-model approach and dimension-invariant observation abstractions. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 428,148 |
1702.02497 | Two-Dimensional AoD and AoA Acquisition for Wideband mmWave Systems with
Cross-Polarized MIMO | In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with cross-polarized antenna elements. The key ingredient of the proposed method is to form custom designed beam pairs, and devise an invertible function of the AoD/AoA to be estimated from the corresponding beam pairs. Further, a new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with cross-polarized antenna elements. To facilitate feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed aiming at feedback reduction for the two-dimensional angle estimation. Numerical results demonstrate that by using the proposed method, good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 67,986 |
2210.16110 | Towards prediction of turbulent flows at high Reynolds numbers using
high performance computing data and deep learning | In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 327,228 |
0911.5046 | Integrating the Probabilistic Models BM25/BM25F into Lucene | This document describes the BM25 and BM25F implementation using the Lucene Java Framework. Both models have stood out at TREC by their performance and are considered as state-of-the-art in the IR community. BM25 is applied to retrieval on plain text documents, that is for documents that do not contain fields, while BM25F is applied to documents with structure. | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 5,028 |
1212.4653 | Convolutional Codes Derived From Group Character Codes | New families of unit memory as well as multi-memory convolutional codes are constructed algebraically in this paper. These convolutional codes are derived from the class of group character codes. The proposed codes have basic generator matrices, consequently, they are non catastrophic. Additionally, the new code parameters are better than the ones available in the literature. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 20,477 |
1502.04623 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | true | false | false | 40,287 |
1611.03679 | Deep Convolutional Neural Network for Inverse Problems in Imaging | In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 63,730 |
1011.2113 | Complexity Adjusted Soft-Output Sphere Decoding by Adaptive LLR Clipping | A-posteriori probability (APP) receivers operating over multiple-input, multiple-output channels provide enhanced bit error rate (BER) performance at the cost of increased complexity. However, employing full APP processing over favorable transmission environments, where less efficient approaches may already provide the required performance at a reduced complexity, results in unnecessary processing. For slowly varying channel statistics substantial complexity savings can be achieved by simple adaptive schemes. Such schemes track the BER performance and adjust the complexity of the soft output sphere decoder by adaptively setting the related log-likelihood ratio (LLR) clipping value. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 8,182 |
2209.07780 | Computing Forward Reachable Sets for Nonlinear Adaptive Multirotor
Controllers | In multirotor systems, guaranteeing safety while considering unknown disturbances is essential for robust trajectory planning. The Forward reachable set (FRS), the set of feasible states subject to bounded disturbances, can be utilized to identify robust and collision-free trajectories by checking the intersections with obstacles. However, in many cases, the FRS is not calculated in real time and is too conservative to be used in actual applications. In this paper, we address these issues by introducing a nonlinear disturbance observer (NDOB) and an adaptive controller to the multirotor system. We express the FRS of the closed-loop multirotor system with an adaptive controller in augmented state space using Hamilton-Jacobi reachability analysis. Then, we derive a closed-form expression that over-approximates the FRS as an ellipsoid, allowing for real-time computation. By compensating for disturbances with the adaptive controller, our over-approximated FRS can be smaller than other ellipsoidal over-approximations. Numerical examples validate the computational efficiency and the smaller scale of our proposed FRS. | false | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | 317,884 |
2410.02476 | Online Convex Optimization with a Separation Oracle | In this paper, we introduce a new projection-free algorithm for Online Convex Optimization (OCO) with a state-of-the-art regret guarantee among separation-based algorithms. Existing projection-free methods based on the classical Frank-Wolfe algorithm achieve a suboptimal regret bound of $O(T^{3/4})$, while more recent separation-based approaches guarantee a regret bound of $O(\kappa \sqrt{T})$, where $\kappa$ denotes the asphericity of the feasible set, defined as the ratio of the radii of the containing and contained balls. However, for ill-conditioned sets, $\kappa$ can be arbitrarily large, potentially leading to poor performance. Our algorithm achieves a regret bound of $\widetilde{O}(\sqrt{dT} + \kappa d)$, while requiring only $\widetilde{O}(1)$ calls to a separation oracle per round. Crucially, the main term in the bound, $\widetilde{O}(\sqrt{d T})$, is independent of $\kappa$, addressing the limitations of previous methods. Additionally, as a by-product of our analysis, we recover the $O(\kappa \sqrt{T})$ regret bound of existing OCO algorithms with a more straightforward analysis and improve the regret bound for projection-free online exp-concave optimization. Finally, for constrained stochastic convex optimization, we achieve a state-of-the-art convergence rate of $\widetilde{O}(\sigma/\sqrt{T} + \kappa d/T)$, where $\sigma$ represents the noise in the stochastic gradients, while requiring only $\widetilde{O}(1)$ calls to a separation oracle per iteration. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 494,291 |
1601.06755 | The Utility of Hedged Assertions in the Emergence of Shared Categorical
Labels | We investigate the emergence of shared concepts in a community of language users using a multi-agent simulation. We extend results showing that negated assertions are of use in developing shared categories, to include assertions modified by linguistic hedges. Results show that using hedged assertions positively affects the emergence of shared categories in two distinct ways. Firstly, using contraction hedges like `very' gives better convergence over time. Secondly, using expansion hedges such as `quite' reduces concept overlap. However, both these improvements come at a cost of slower speed of development. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | true | false | false | false | 51,334 |
2009.02627 | Preserving Privacy of the Influence Structure in Friedkin-Johnsen
Systems | The nature of information sharing in common distributed consensus algorithms permits network eavesdroppers to expose sensitive system information. An important parameter within distributed systems, often neglected under the scope of privacy preservation, is the influence structure - the weighting each agent places on the sources of their opinion pool. This paper proposes a local (i.e. computed individually by each agent), time varying mask to prevent the discovery of the influence structure by an external observer with access to the entire information flow, network knowledge and mask formulation. This result is produced through the auxiliary demonstration of the preserved stability of a Friedkin-Johnsen system under a set of generalised conditions. The mask is developed under these constraints and involves perturbing the influence structure by decaying pseudonoise. This paper provides the information matrix of the best influence structure estimate by an eavesdropper lacking a priori knowledge and uses stochastic simulations to analyse the performance of the mask against ranging system hyperparameters. | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 194,604 |
2107.06277 | Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit
Partial Observability | Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL. We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs. Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for approximately solving the partially observed problem. Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves significant gains in generalization over current methods on the Procgen benchmark suite. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 246,044 |
2312.10105 | SeiT++: Masked Token Modeling Improves Storage-efficient Training | Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. However, achieving highly generalizable and high-performing vision models requires expansive datasets, resulting in significant storage requirements. This storage challenge is a critical bottleneck for scaling up models. A recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i.e., tokens) as network inputs for vision classification. This approach achieved 90% of the performance of a model trained on full-pixel images with only 1% of the storage. While SeiT needs labeled data, its potential in scenarios beyond fully supervised learning remains largely untapped. In this paper, we extend SeiT by integrating Masked Token Modeling (MTM) for self-supervised pre-training. Recognizing that self-supervised approaches often demand more data due to the lack of labels, we introduce TokenAdapt and ColorAdapt. These methods facilitate comprehensive token-friendly data augmentation, effectively addressing the increased data requirements of self-supervised learning. We evaluate our approach across various scenarios, including storage-efficient ImageNet-1k classification, fine-grained classification, ADE-20k semantic segmentation, and robustness benchmarks. Experimental results demonstrate consistent performance improvement in diverse experiments, validating the effectiveness of our method. Code is available at https://github.com/naver-ai/seit. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 416,016 |
1410.2871 | An Ontology for Comprehensive Tutoring of Euphonic Conjunctions of
Sanskrit Grammar | Euphonic conjunctions (sandhis) form a very important aspect of Sanskrit morphology and phonology. The traditional and modern methods of studying about euphonic conjunctions in Sanskrit follow different methodologies. The former involves a rigorous study of the Paninian system embodied in Panini's Ashtadhyayi, while the latter usually involves the study of a few important sandhi rules with the use of examples. The former is not suitable for beginners, and the latter, not sufficient to gain a comprehensive understanding of the operation of sandhi rules. This is so since there are not only numerous sandhi rules and exceptions, but also complex precedence rules involved. The need for a new ontology for sandhi-tutoring was hence felt. This work presents a comprehensive ontology designed to enable a student-user to learn in stages all about euphonic conjunctions and the relevant aphorisms of Sanskrit grammar and to test and evaluate the progress of the student-user. The ontology forms the basis of a multimedia sandhi tutor that was given to different categories of users including Sanskrit scholars for extensive and rigorous testing. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 36,658 |
1808.07452 | Generalized Canonical Polyadic Tensor Decomposition | Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback-Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety statistically-motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. We demonstrate the flexibility of GCP on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 105,744 |
2201.13290 | Model-Based Engineering of CPPS Functions and Code Generation for Skills | Today's production systems are complex networks of cyber-physical systems which combine mechanical and electronic parts with software and networking capabilities. To the inherent complexity of such systems additional complexity arises from the context in which these systems operate. Manufacturing companies need to be able to adapt their production to ever changing customer demands as well as decreasing lot sizes. Engineering such systems, which need to be combined and reconfigured into different networks under changing conditions, requires engineering methods to carefully design them for possible future uses. Such engineering methods need to preserve the flexibility of functions into runtime, so that reconfiguring machines can be done with as little effort as possible. In this paper we present a model-based approach that is focused on machine functions and allows to methodically develop system functionalities for changing system networks. These functions are implemented as so-called skills using automated code-generation. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | 277,936 |
2407.21658 | Beat this! Accurate beat tracking without DBN postprocessing | We propose a system for tracking beats and downbeats with two objectives: generality across a diverse music range, and high accuracy. We achieve generality by training on multiple datasets -- including solo instrument recordings, pieces with time signature changes, and classical music with high tempo variations -- and by removing the commonly used Dynamic Bayesian Network (DBN) postprocessing, which introduces constraints on the meter and tempo. For high accuracy, among other improvements, we develop a loss function tolerant to small time shifts of annotations, and an architecture alternating convolutions with transformers either over frequency or time. Our system surpasses the current state of the art in F1 score despite using no DBN. However, it can still fail, especially for difficult and underrepresented genres, and performs worse on continuity metrics, so we publish our model, code, and preprocessed datasets, and invite others to beat this. | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 477,625 |
1602.08977 | Clustering Based Feature Learning on Variable Stars | The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These descriptors commonly demand significant computational power to calculate, require substantial research effort to develop and do not guarantee good performance on the final classification task. Today, lightcurve representation is not entirely automatic; algorithms that extract lightcurve features are designed by humans and must be manually tuned up for every survey. The vast amounts of data that will be generated in future surveys like LSST mean astronomers must develop analysis pipelines that are both scalable and automated. Recently, substantial efforts have been made in the machine learning community to develop methods that prescind from expert-designed and manually tuned features for features that are automatically learned from data. In this work we present what is, to our knowledge, the first unsupervised feature learning algorithm designed for variable stars. Our method first extracts a large number of lightcurve subsequences from a given set of photometric data, which are then clustered to find common local patterns in the time series. Representatives of these patterns, called exemplars, are then used to transform lightcurves of a labeled set into a new representation that can then be used to train an automatic classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias generated when the learning process is done only with labeled data. We test our method on MACHO and OGLE datasets; the results show that the classification performance we achieve is as good and in some cases better than the performance achieved using traditional features, while the computational cost is significantly lower. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 52,712 |
1408.3750 | Real-time emotion recognition for gaming using deep convolutional
network features | The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to other published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these features tracks the facial expressions of a player in real-time. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | true | false | false | 35,410 |
2210.01314 | Meta Navigation Functions: Adaptive Associations for Coordination of
Multi-Agent Systems | In this paper, we introduce a new class of potential fields, i.e., meta navigation functions (MNFs) to coordinate multi-agent systems. Thanks to the MNF formulation, agents can contribute to each other's coordination via partial and/or total associations, contrary to traditional decentralized navigation functions (DNFs). In particular, agents may stimulate each other via their MNFs. Moreover, MNFs need to be confined which is a weaker condition compared to the Morse condition of DNFs. An MNF is composed of a confined function and an attraction kernel. The critical points of the former can be confined in a safe region around a target critical point. The collision-free trajectory of an agent and its associations to its peers are governed by a confined function before reaching its safe region. Then, the attraction kernel drives the agent to its target in the safe region. MNFs provide faster coordination compared to DNFs. We illustrate how MNFs may exhibit some social behaviors in the course of partial and total associations among agents. Our simulations verify the efficiency of MNFs to coordinate complex swarms of agents. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | 321,211 |
2206.10491 | Bi-Calibration Networks for Weakly-Supervised Video Representation
Learning | The leverage of large volumes of web videos paired with the searched queries or surrounding texts (e.g., title) offers an economic and extensible alternative to supervised video representation learning. Nevertheless, modeling such weakly visual-textual connection is not trivial due to query polysemy (i.e., many possible meanings for a query) and text isomorphism (i.e., same syntactic structure of different text). In this paper, we introduce a new design of mutual calibration between query and text to boost weakly-supervised video representation learning. Specifically, we present Bi-Calibration Networks (BCN) that novelly couples two calibrations to learn the amendment from text to query and vice versa. Technically, BCN executes clustering on all the titles of the videos searched by an identical query and takes the centroid of each cluster as a text prototype. The query vocabulary is built directly on query words. The video-to-text/video-to-query projections over text prototypes/query vocabulary then start the text-to-query or query-to-text calibration to estimate the amendment to query or text. We also devise a selection scheme to balance the two corrections. Two large-scale web video datasets paired with query and title for each video are newly collected for weakly-supervised video representation learning, which are named as YOVO-3M and YOVO-10M, respectively. The video features of BCN learnt on 3M web videos obtain superior results under linear model protocol on downstream tasks. More remarkably, BCN trained on the larger set of 10M web videos with further fine-tuning leads to 1.6%, and 1.8% gains in top-1 accuracy on Kinetics-400, and Something-Something V2 datasets over the state-of-the-art TDN, and ACTION-Net methods with ImageNet pre-training. Source code and datasets are available at \url{https://github.com/FuchenUSTC/BCN}. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 303,925 |
2406.02807 | Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for
Fast Collision Checking | Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision checking. We present a novel spatial data structure, the collision-affording point tree (CAPT): an exact representation of point clouds that accelerates collision-checking queries between robots and point clouds by an order of magnitude, with an average query time of less than 10 nanoseconds on 3D scenes comprising thousands of points. With the CAPT, sampling-based planners can generate valid, high-quality paths in under a millisecond, with total end-to-end computation time faster than 60 FPS, on a single thread of a consumer-grade CPU. We also present a point cloud filtering algorithm, based on space-filling curves, which reduces the number of points in a point cloud while preserving structure. Our approach enables robots to plan at real-time speeds in sensed environments, opening up potential uses of planning for high-dimensional systems in dynamic, changing, and unmodeled environments. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 460,948 |
1710.00239 | $\kappa$-PMP: Enhancing Physics-based Motion Planners with
Knowledge-based Reasoning | Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called \k{appa}-PMP can be used with any kinodynamic planner, thus giving rise to e.g. \k{appa}-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths. | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | 81,832 |
2304.08855 | A Domain-Region Based Evaluation of ML Performance Robustness to
Covariate Shift | Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input-output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier's performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias towards the region with high density in the input space domain of the training samples. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 358,845 |
2210.04525 | SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup
Training | The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in real-world applications, label noise inevitably exists in training data, damaging the effectiveness, robustness, and generalization of the models constructed on such data. Recently, remarkable achievements have been made to mitigate this dilemma in visual data, while only a few explore textual data. To fill this gap, we present SelfMix, a simple yet effective method, to handle label noise in text classification tasks. SelfMix uses the Gaussian Mixture Model to separate samples and leverages semi-supervised learning. Unlike previous works requiring multiple models, our method utilizes the dropout mechanism on a single model to reduce the confirmation bias in self-training and introduces a textual-level mixup training strategy. Experimental results on three text classification benchmarks with different types of text show that the performance of our proposed method outperforms these strong baselines designed for both textual and visual data under different noise ratios and noise types. Our code is available at https://github.com/noise-learning/SelfMix. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 322,489 |
1504.01552 | Hybrid Scheduling/Signal-Level Coordination in the Downlink of
Multi-Cloud Radio-Access Networks | In the context of resource allocation in cloud-radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio-access network, as a means to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 41,830 |
2203.16799 | M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in
Conversations | Expression of emotions is a crucial part of daily human communication. Emotion recognition in conversations (ERC) is an emerging field of study, where the primary task is to identify the emotion behind each utterance in a conversation. Though a lot of work has been done on ERC in the past, these works only focus on ERC in the English language, thereby ignoring any other languages. In this paper, we present Multilingual MELD (M-MELD), where we extend the Multimodal EmotionLines Dataset (MELD) \cite{poria2018meld} to 4 other languages beyond English, namely Greek, Polish, French, and Spanish. Beyond just establishing strong baselines for all of these 4 languages, we also propose a novel architecture, DiscLSTM, that uses both sequential and conversational discourse context in a conversational dialogue for ERC. Our proposed approach is computationally efficient, can transfer across languages using just a cross-lingual encoder, and achieves better performance than most uni-modal text approaches in the literature on both MELD and M-MELD. We make our data and code publicly on GitHub. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 288,930 |
2005.00207 | Generating Randomness from a Computable, Non-random Sequence of Qubits | Nies and Scholz introduced the notion of a state to describe an infinite sequence of qubits and defined quantum-Martin-Lof randomness for states, analogously to the well known concept of Martin-L\"of randomness for elements of Cantor space (the space of infinite sequences of bits). We formalize how 'measurement' of a state in a basis induces a probability measure on Cantor space. A state is 'measurement random' (mR) if the measure induced by it, under any computable basis, assigns probability one to the set of Martin-L\"of randoms. Equivalently, a state is mR if and only if measuring it in any computable basis yields a Martin-L\"of random with probability one. While quantum-Martin-L\"of random states are mR, the converse fails: there is a mR state, x which is not quantum-Martin-L\"of random. In fact, something stronger is true. While x is computable and can be easily constructed, measuring it in any computable basis yields an arithmetically random sequence with probability one. I.e., classical arithmetic randomness can be generated from a computable, non-quantum random sequence of qubits. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 175,166 |
2111.05987 | Tight bounds for minimum l1-norm interpolation of noisy data | We provide matching upper and lower bounds of order $\sigma^2/\log(d/n)$ for the prediction error of the minimum $\ell_1$-norm interpolator, a.k.a. basis pursuit. Our result is tight up to negligible terms when $d \gg n$, and is the first to imply asymptotic consistency of noisy minimum-norm interpolation for isotropic features and sparse ground truths. Our work complements the literature on "benign overfitting" for minimum $\ell_2$-norm interpolation, where asymptotic consistency can be achieved only when the features are effectively low-dimensional. | false | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | 265,942 |
2501.14014 | INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for
Blind and Non-Blind Image Restoration | Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfactory results, IR methods based on diffusion models present several limitations. First of all, most non-blind approaches require an analytical expression of the degradation model to guide the sampling process. Secondly, most existing blind approaches rely on families of pre-defined degradation models for training their deep networks. The above issues limit the flexibility of these approaches and so their ability to handle real-world degradation tasks. In this paper, we propose a novel INN-guided probabilistic diffusion algorithm for non-blind and blind image restoration, namely INDIGO and BlindINDIGO, which combines the merits of the perfect reconstruction property of invertible neural networks (INN) with the strong generative capabilities of pre-trained diffusion models. Specifically, we train the forward process of the INN to simulate an arbitrary degradation process and use the inverse to obtain an intermediate image that we use to guide the reverse diffusion sampling process through a gradient step. We also introduce an initialization strategy, to further improve the performance and inference speed of our algorithm. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually on synthetic and real-world low-quality images. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 526,952 |
2207.03830 | Safe reinforcement learning for multi-energy management systems with
known constraint functions | Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees - resulting in various potentially unsafe interactions within its safety-critical environment. In this paper, we present two novel safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation. These provide hard-constraint, rather than soft- and chance-constraint, satisfaction guarantees both during training a (near) optimal policy (which involves exploratory and exploitative, i.e. greedy, steps) as well as during deployment of any policy (e.g. random agents or offline trained RL agents). This without the need of solving a mathematical program, resulting in less computational power requirements and a more flexible constraint function formulation (no derivative information is required). In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility (i.e. useful policy) compared to a vanilla RL benchmark and Optlayer benchmark (94,6% and 82,8% compared to 35,5% and 77,8%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques applicable beyond RL, as demonstrated with random policies while still providing hard-constraint guarantees. | false | false | false | false | true | false | true | false | false | false | true | false | false | false | false | false | false | false | 306,990 |
2002.07965 | Being Bayesian about Categorical Probability | Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the presumed noise inherent in the observed label, which provides consistent gains in generalization performance in multiple challenging tasks. The proposed method inherits advantages of Bayesian approaches that achieve better uncertainty estimation and model calibration. Our method can be implemented as a plug-and-play loss function with negligible computational overhead compared to the softmax with the cross-entropy loss function. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 164,619 |
2410.13924 | ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding | The performance of neural networks scales with both their size and the amount of data they have been trained on. This is shown in both language and image generation. However, this requires scaling-friendly network architectures as well as large-scale datasets. Even though scaling-friendly architectures like transformers have emerged for 3D vision tasks, the GPT-moment of 3D vision remains distant due to the lack of training data. In this paper, we introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. Specifically, we complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. To this end, we extend LabelMaker, a recent automatic annotation pipeline, to serve the needs of large-scale pre-training. This involves extending the pipeline with cutting-edge segmentation models as well as making it robust to the challenges of large-scale processing. Further, we push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models, demonstrating the efficacy of our generated dataset. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 499,778 |
1907.00250 | Multi-objective multi-generation Gaussian process optimizer for design
optimization | We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other more recent algorithms. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 136,980 |
2410.15441 | A Global Coordinate-Free Approach to Invariant Contraction on
Homogeneous Manifolds | In this work, we provide a global condition for contraction with respect to an invariant Riemannian metric on reductive homogeneous spaces. Using left-invariant frames, vector fields on the manifold are horizontally lifted to the ambient Lie group, where the Levi-Civita connection is globally characterized as a real matrix multiplication. By linearizing in these left-invariant frames, we characterize contraction using matrix measures on real square matrices, avoiding the use of local charts. Applying this global condition, we provide a necessary condition for a prescribed subset of the manifold to possibly admit a contracting system with respect to an invariant metric. Applied to the sphere, this condition implies that no closed hemisphere can be contained in a contraction region. Finally, we apply our results to compute reachable sets for an attitude control problem. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 500,540 |
2401.18012 | Causal Coordinated Concurrent Reinforcement Learning | In this work, we propose a novel algorithmic framework for data sharing and coordinated exploration for the purpose of learning more data-efficient and better performing policies under a concurrent reinforcement learning (CRL) setting. In contrast to other work which make the assumption that all agents act under identical environments, we relax this restriction and instead consider the formulation where each agent acts within an environment which shares a global structure but also exhibits individual variations. Our algorithm leverages a causal inference algorithm in the form of Additive Noise Model - Mixture Model (ANM-MM) in extracting model parameters governing individual differentials via independence enforcement. We propose a new data sharing scheme based on a similarity measure of the extracted model parameters and demonstrate superior learning speeds on a set of autoregressive, pendulum and cart-pole swing-up tasks and finally, we show the effectiveness of diverse action selection between common agents under a sparse reward setting. To the best of our knowledge, this is the first work in considering non-identical environments in CRL and one of the few works which seek to integrate causal inference with reinforcement learning (RL). | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 425,398 |
2011.05784 | Generative and Discriminative Learning for Distorted Image Restoration | Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task. To edit images in an efficient way, distorted images are expected to be restored automatically. This paper aims at the distorted image restoration, which is characterized by seeking the appropriate warping and completion of a distorted image. Existing methods focus on the hardware assistance or the geometric principle to solve the specific regular deformation caused by natural phenomena, but they cannot handle the irregularity and uncertainty of artificial distortion in this task. To address this issue, we propose a novel generative and discriminative learning method based on deep neural networks, which can learn various reconstruction mappings and represent complex and high-dimensional data. This method decomposes the task into a rectification stage and a refinement stage. The first stage generative network predicts the mapping from the distorted images to the rectified ones. The second stage generative network then further optimizes the perceptual quality. Since there is no available dataset or benchmark to explore this task, we create a Distorted Face Dataset (DFD) by forward distortion mapping based on CelebA dataset. Extensive experimental evaluation on the proposed benchmark and the application demonstrates that our method is an effective way for distorted image restoration. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 206,040 |
2305.17347 | CGELBank Annotation Manual v1.1 | CGELBank is a treebank and associated tools based on a syntactic formalism for English derived from the Cambridge Grammar of the English Language. This document lays out the particularities of the CGELBank annotation scheme. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 368,539 |
0901.1444 | Algebraic gossip on Arbitrary Networks | Consider a network of nodes where each node has a message to communicate to all other nodes. For this communication problem, we analyze a gossip based protocol where coded messages are exchanged. This problem was studied by Aoyama and Shah where a bound to the dissemination time based on the spectral properties of the underlying communication graph is provided. Our contribution is a uniform bound that holds for arbitrary networks. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 2,922 |
2306.07526 | User-defined Event Sampling and Uncertainty Quantification in Diffusion
Models for Physical Dynamical Systems | Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We demonstrate that these models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. In these applications, diffusion models can implicitly represent knowledge about outliers and extreme events; however, querying that knowledge through conditional sampling or measuring probabilities is surprisingly difficult. Existing methods for conditional sampling at inference time seek mainly to enforce the constraints, which is insufficient to match the statistics of the distribution or compute the probability of the chosen events. To achieve these ends, optimally one would use the conditional score function, but its computation is typically intractable. In this work, we develop a probabilistic approximation scheme for the conditional score function which provably converges to the true distribution as the noise level decreases. With this scheme we are able to sample conditionally on nonlinear userdefined events at inference time, and matches data statistics even when sampling from the tails of the distribution. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 373,048 |
2211.14073 | EDGAR: Embedded Detection of Gunshots by AI in Real-time | Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting problem in a generic way through machine learning. In this study, we describe a method allowing one to construct a dataset with minimal labelling effort by only requiring the total number of shots fired in a time series. To our knowledge, this is the first study to propose a technique, based on learning from label proportions, that is able to exploit these weak labels to derive an instance-level classifier able to solve the counting problem and the more general discrimination problem. We also show that this technique can be deployed in heavily constrained microcontrollers while still providing hard real-time (<100ms) inference. We evaluate our technique against a state-of-the-art unsupervised algorithm and show a sizeable improvement, suggesting that the information from the weak labels is successfully leveraged. Finally, we evaluate our technique against human-generated state-of-the-art algorithms and show that it provides comparable performance and significantly outperforms them in some offline and real-world benchmarks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 332,710 |
2412.06956 | Microcontroller-Driven MPPT System for Enhanced Photovoltaic Efficiency:
An Experimental Approach in Nepal | Solar energy utilization in places like Nepal, is often obstructed by unpredicted environmental factors and existing technological barriers. The challenges encountered often result in fluctuating energy outputs, hindering the transition to greener energy solutions. To tackle these issues, this study introduces a custom-designed Maximum Power Point Tracking (MPPT) controller, seamlessly incorporated into a microcontroller-based battery charging system. This approach seeks to enhance the efficiency of photovoltaic (PV) systems, aligning with the global shift towards renewables. The research's primary objective is to enhance PV module power yield employing MPPT techniques, thereby reducing dependency on non-renewable energy sources. Key goals include real-time MPP tracking for optimal power extraction from PV modules and the integration of a real-time monitoring mechanism for PV and battery states. Leveraging a coordinated interplay of sensors measuring temperature, voltage, and current, vital metrics are fed to the microcontroller. This, in turn, generates a precise Pulse Width Modulation (PWM) signal, fine-tuning the voltage regulation of the buck-boost converter Metal Oxide Semiconductor Field Effect Transistor (MOSFET) for optimal operation. The adopted approach emphasizes monitoring environmental metrics, overseeing power outputs, and generating PWM signals to adeptly manage the buck-boost converter MOSFET voltage. Concurrently, data is transmitted hourly to a cloud platform, facilitating real-time monitoring capabilities showcasing the IoT application. As a result of these integrations, an efficiency improvement of approximately 37.28% was observed. In essence, this research underscores the profound impact of merging advanced technologies within the renewable energy sector, offering a robust blueprint for enhancing energy stability and productivity. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 515,452 |
2406.19801 | MulTi-Wise Sampling: Trading Uniform T-Wise Feature Interaction Coverage
for Smaller Samples | Ensuring the functional safety of highly configurable systems often requires testing representative subsets of all possible configurations to reduce testing effort and save resources. The ratio of covered t-wise feature interactions (i.e., T-Wise Feature Interaction Coverage) is a common criterion for determining whether a subset of configurations is representative and capable of finding faults. Existing t-wise sampling algorithms uniformly cover t-wise feature interactions for all features, resulting in lengthy execution times and large sample sizes, particularly when large t-wise feature interactions are considered (i.e., high values of t). In this paper, we introduce a novel approach to t-wise feature interaction sampling, questioning the necessity of uniform coverage across all t-wise feature interactions, called \emph{\mulTiWise{}}. Our approach prioritizes between subsets of critical and non-critical features, considering higher t-values for subsets of critical features when generating a t-wise feature interaction sample. We evaluate our approach using subject systems from real-world applications, including \busybox{}, \soletta{}, \fiasco{}, and \uclibc{}. Our results show that sacrificing uniform t-wise feature interaction coverage between all features reduces the time needed to generate a sample and the resulting sample size. Hence, \mulTiWise{} Sampling offers an alternative to existing approaches if knowledge about feature criticality is available. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 468,570 |
2110.04961 | Equivalence Analysis between Counterfactual Regret Minimization and
Online Mirror Descent | Follow-the-Regularized-Lead (FTRL) and Online Mirror Descent (OMD) are regret minimization algorithms for Online Convex Optimization (OCO), they are mathematically elegant but less practical in solving Extensive-Form Games (EFGs). Counterfactual Regret Minimization (CFR) is a technique for approximating Nash equilibria in EFGs. CFR and its variants have a fast convergence rate in practice, but their theoretical results are not satisfactory. In recent years, researchers have been trying to link CFRs with OCO algorithms, which may provide new theoretical results and inspire new algorithms. However, existing analysis is restricted to local decision points. In this paper, we show that CFRs with Regret Matching and Regret Matching+ are equivalent to special cases of FTRL and OMD, respectively. According to these equivalences, a new FTRL and a new OMD algorithm, which can be considered as extensions of vanilla CFR and CFR+, are derived. The experimental results show that the two variants converge faster than conventional FTRL and OMD, even faster than vanilla CFR and CFR+ in some EFGs. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 260,106 |
2106.12336 | First Step Towards EXPLAINable DGA Multiclass Classification | Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server. Counteracting DGAs, several machine learning classifiers have been proposed enabling the identification of the DGA that generated a specific domain name and thus triggering targeted remediation measures. However, the proposed state-of-the-art classifiers are based on deep learning models. The black box nature of these makes it difficult to evaluate their reasoning. The resulting lack of confidence makes the utilization of such models impracticable. In this paper, we propose EXPLAIN, a feature-based and contextless DGA multiclass classifier. We comparatively evaluate several combinations of feature sets and hyperparameters for our approach against several state-of-the-art classifiers in a unified setting on the same real-world data. Our classifier achieves competitive results, is real-time capable, and its predictions are easier to trace back to features than the predictions made by the DGA multiclass classifiers proposed in related work. | false | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | 242,701 |
2303.01389 | Machine Learning-Based Detection of Parkinson's Disease From
Resting-State EEG: A Multi-Center Study | Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically validated frequency bands, and feature selection before evaluating the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 348,950 |
2311.02549 | 3D-Aware Talking-Head Video Motion Transfer | Motion transfer of talking-head videos involves generating a new video with the appearance of a subject video and the motion pattern of a driving video. Current methodologies primarily depend on a limited number of subject images and 2D representations, thereby neglecting to fully utilize the multi-view appearance features inherent in the subject video. In this paper, we propose a novel 3D-aware talking-head video motion transfer network, Head3D, which fully exploits the subject appearance information by generating a visually-interpretable 3D canonical head from the 2D subject frames with a recurrent network. A key component of our approach is a self-supervised 3D head geometry learning module, designed to predict head poses and depth maps from 2D subject video frames. This module facilitates the estimation of a 3D head in canonical space, which can then be transformed to align with driving video frames. Additionally, we employ an attention-based fusion network to combine the background and other details from subject frames with the 3D subject head to produce the synthetic target video. Our extensive experiments on two public talking-head video datasets demonstrate that Head3D outperforms both 2D and 3D prior arts in the practical cross-identity setting, with evidence showing it can be readily adapted to the pose-controllable novel view synthesis task. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 405,484 |
2401.00313 | Matching of Users and Creators in Two-Sided Markets with Departures | Many online platforms of today, including social media sites, are two-sided markets bridging content creators and users. Most of the existing literature on platform recommendation algorithms largely focuses on user preferences and decisions, and does not simultaneously address creator incentives. We propose a model of content recommendation that explicitly focuses on the dynamics of user-content matching, with the novel property that both users and creators may leave the platform permanently if they do not experience sufficient engagement. In our model, each player decides to participate at each time step based on utilities derived from the current match: users based on alignment of the recommended content with their preferences, and creators based on their audience size. We show that a user-centric greedy algorithm that does not consider creator departures can result in arbitrarily poor total engagement, relative to an algorithm that maximizes total engagement while accounting for two-sided departures. Moreover, in stark contrast to the case where only users or only creators leave the platform, we prove that with two-sided departures, approximating maximum total engagement within any constant factor is NP-hard. We present two practical algorithms, one with performance guarantees under mild assumptions on user preferences, and another that tends to outperform algorithms that ignore two-sided departures in practice. | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 418,949 |
1204.2447 | On Capacity Regions of Discrete Asynchronous Multiple Access Channels | A general formalization is given for asynchronous multiple access channels which admits different assumptions on delays. This general framework allows the analysis of so far unexplored models leading to new interesting capacity regions. In particular, a single letter characterization is given for the capacity region in case of 3 senders, 2 synchronous with each other and the third not synchronous with them. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 15,414 |
2208.03232 | Driving Points Prediction For Abdominal Probabilistic Registration | Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological heterogeneity and variability of the human abdomen. Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images. These probabilistic models are informative and robust while allowing large displacements by design. As the displacement distributions are typically estimated on a subset of points (which we refer to as driving points), due to computational requirements, we propose in this work to learn a driving points predictor. Compared to previously proposed methods, the driving points predictor is optimized in an end-to-end fashion to infer driving points tailored for a specific registration pipeline. We evaluate the impact of our contribution on two different datasets corresponding to different modalities. Specifically, we compared the performances of 6 different probabilistic displacement registration models when using a driving points predictor or one of 2 other standard driving points selection methods. The proposed method improved performances in 11 out of 12 experiments. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 311,719 |
1904.12483 | Self-Attention Capsule Networks for Object Classification | We propose a novel architecture for object classification, called Self-Attention Capsule Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as an integral layer within the Capsule Network (CapsNet). While the Self-Attention mechanism supplies a long-range dependencies, results in selecting the more dominant image regions to focus on, the CapsNet analyzes the relevant features and their spatial correlations inside these regions only. The features are extracted in the convolutional layer. Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task. The attention map is then fed into the CapsNet primary layer that is followed by a classification layer. The proposed SACN model was designed to solve two main limitations of the baseline CapsNet - analysis of complex data and significant computational load. In this work, we use a shallow CapsNet architecture and compensates for the absence of a deeper network by using the Self-Attention module to significantly improve the results. The proposed Self-Attention CapsNet architecture was extensively evaluated on six different datasets, mainly on three different medical sets, in addition to the natural MNIST, SVHN and CIFAR10. The model was able to classify images and their patches with diverse and complex backgrounds better than the baseline CapsNet. As a result, the proposed Self-Attention CapsNet significantly improved classification performance within and across different datasets and outperformed the baseline CapsNet, ResNet-18 and DenseNet-40 not only in classification accuracy but also in robustness. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 129,123 |
2108.06270 | Enhancing audio quality for expressive Neural Text-to-Speech | Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings. However, not all speaking styles are easy to model: highly expressive voices are still challenging even to recent TTS architectures since there seems to be a trade-off between expressiveness in a generated audio and its signal quality. In this paper, we present a set of techniques that can be leveraged to enhance the signal quality of a highly-expressive voice without the use of additional data. The proposed techniques include: tuning the autoregressive loop's granularity during training; using Generative Adversarial Networks in acoustic modelling; and the use of Variational Auto-Encoders in both the acoustic model and the neural vocoder. We show that, when combined, these techniques greatly closed the gap in perceived naturalness between the baseline system and recordings by 39% in terms of MUSHRA scores for an expressive celebrity voice. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 250,554 |
1312.4986 | A Comparative Evaluation of Curriculum Learning with Filtering and
Boosting | Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Several machine learning techniques treat instances in a data set differently during training such as curriculum learning, filtering, and boosting. However, an automated method for determining how beneficial an instance is for inferring a model of the data does not exist. In this paper, we present an automated method that orders the instances in a data set by complexity based on the their likelihood of being misclassified (instance hardness). The underlying assumption of this method is that instances with a high likelihood of being misclassified represent more complex concepts in a data set. Ordering the instances in a data set allows a learning algorithm to focus on the most beneficial instances and ignore the detrimental ones. We compare ordering the instances in a data set in curriculum learning, filtering and boosting. We find that ordering the instances significantly increases classification accuracy and that filtering has the largest impact on classification accuracy. On a set of 52 data sets, ordering the instances increases the average accuracy from 81% to 84%. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 29,193 |
1707.01555 | A Deep Network with Visual Text Composition Behavior | While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | true | false | false | 76,561 |
1708.02000 | Analysis of Social Group Dynamics | In this thesis the method for social group evolution discovery, called GED, is analyzed. Especially, GED method is compared with other methods tracking changes in groups over time with focus on accuracy, computational cost, ease of implementation and flexibility of the methods. The methods are evaluated on overlapping and disjoint social groups. Finally, GED method is run with different user importance measures. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 78,501 |
2210.07475 | Latent Temporal Flows for Multivariate Analysis of Wearables Data | Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes in an individual's health profile. Indeed, machine learning models for sensor signals have enabled a diverse range of healthcare related applications including early detection of abnormalities, fertility tracking, and adverse drug effect prediction. However, these models can fail to account for the dependent high-dimensional nature of the underlying sensor signals. In this paper, we introduce Latent Temporal Flows, a method for multivariate time-series modeling tailored to this setting. We assume that a set of sequences is generated from a multivariate probabilistic model of an unobserved time-varying low-dimensional latent vector. Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows. Using data from the Apple Heart and Movement Study (AH&MS), we illustrate promising forecasting performance on these challenging signals. Additionally, by analyzing two and three dimensional representations learned by our model, we show that we can identify participants' $\text{VO}_2\text{max}$, a main indicator and summary of cardio-respiratory fitness, using only lower-level signals. Finally, we show that the proposed method consistently outperforms the state-of-the-art in multi-step forecasting benchmarks (achieving at least a $10\%$ performance improvement) on several real-world datasets, while enjoying increased computational efficiency. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 323,728 |
1909.03587 | Statistical Modelling of the Clipping Noise in OFDM-based Visible Light
Communication System | This paper analyses the statistics of the clipping noise in orthogonal frequency-division-multiplex (OFDM) based visible light Communication systems. The clipped signal is generally modelled as the summation of the scaled original signal and clipping noise, which is treated by the linear equalizer in the receiver. Generally, it is assumed that the clipped and original signal share the same statistics. Although valid in some cases, we show that such assumption is invalid when the transmitter is tightly constrained. We derive closed-form probability distribution function (pdf) for the clipping noise and use the pdf for statistical hypothesis testing in an optimum receiver | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 144,540 |
2207.01450 | Discourse-Aware Graph Networks for Textual Logical Reasoning | Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts. | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 306,181 |
2404.06633 | Evolving Loss Functions for Specific Image Augmentation Techniques | Previous work in Neural Loss Function Search (NLFS) has shown a lack of correlation between smaller surrogate functions and large convolutional neural networks with massive regularization. We expand upon this research by revealing another disparity that exists, correlation between different types of image augmentation techniques. We show that different loss functions can perform well on certain image augmentation techniques, while performing poorly on others. We exploit this disparity by performing an evolutionary search on five types of image augmentation techniques in the hopes of finding image augmentation specific loss functions. The best loss functions from each evolution were then taken and transferred to WideResNet-28-10 on CIFAR-10 and CIFAR-100 across each of the five image augmentation techniques. The best from that were then taken and evaluated by fine-tuning EfficientNetV2Small on the CARS, Oxford-Flowers, and Caltech datasets across each of the five image augmentation techniques. Multiple loss functions were found that outperformed cross-entropy across multiple experiments. In the end, we found a single loss function, which we called the inverse bessel logarithm loss, that was able to outperform cross-entropy across the majority of experiments. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | false | false | 445,530 |
1306.4886 | Supervised Topical Key Phrase Extraction of News Stories using
Crowdsourcing, Light Filtering and Co-reference Normalization | Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a "Gold Standard" - a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 25,348 |
1912.04177 | Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation | Recently, Musco and Woodruff (FOCS, 2017) showed that given an $n \times n$ positive semidefinite (PSD) matrix $A$, it is possible to compute a $(1+\epsilon)$-approximate relative-error low-rank approximation to $A$ by querying $O(nk/\epsilon^{2.5})$ entries of $A$ in time $O(nk/\epsilon^{2.5} +n k^{\omega-1}/\epsilon^{2(\omega-1)})$. They also showed that any relative-error low-rank approximation algorithm must query $\Omega(nk/\epsilon)$ entries of $A$, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries $O(nk/\epsilon)$ entries of $A$ and outputs a relative-error low-rank approximation in $O(n(k/\epsilon)^{\omega-1})$ time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent $\omega$ is $2$. We then extend our techniques to negative-type distance matrices. Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation which queries $O(nk/\epsilon^{2.5})$ entries and outputs a rank-$(k+4)$ matrix. We show that the bi-criteria guarantee is not necessary and obtain an $O(nk/\epsilon)$ query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including $\ell_1, \ell_2,$ spherical metrics and hypermetrics. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provide the first sublinear time and query algorithms when the corruption on the diagonal entries is bounded. As a special case, we show sample-optimal sublinear time algorithms for low-rank approximation of correlation matrices corrupted by noise. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 156,780 |
2006.10903 | Exploring Weight Importance and Hessian Bias in Model Pruning | Model pruning is an essential procedure for building compact and computationally-efficient machine learning models. A key feature of a good pruning algorithm is that it accurately quantifies the relative importance of the model weights. While model pruning has a rich history, we still don't have a full grasp of the pruning mechanics even for relatively simple problems involving linear models or shallow neural nets. In this work, we provide a principled exploration of pruning by building on a natural notion of importance. For linear models, we show that this notion of importance is captured by covariance scaling which connects to the well-known Hessian-based pruning. We then derive asymptotic formulas that allow us to precisely compare the performance of different pruning methods. For neural networks, we demonstrate that the importance can be at odds with larger magnitudes and proper initialization is critical for magnitude-based pruning. Specifically, we identify settings in which weights become more important despite becoming smaller, which in turn leads to a catastrophic failure of magnitude-based pruning. Our results also elucidate that implicit regularization in the form of Hessian structure has a catalytic role in identifying the important weights, which dictate the pruning performance. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 183,036 |
2106.15876 | Incorporating Domain Knowledge for Extractive Summarization of Legal
Case Documents | Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guidelines from legal experts into an optimization setup. We conduct detailed experiments over case documents from the Indian Supreme Court. The experiments show that our proposed unsupervised method outperforms several strong baselines in terms of ROUGE scores, including both general summarization algorithms and legal-specific ones. In fact, though our proposed algorithm is unsupervised, it outperforms several supervised summarization models that are trained over thousands of document-summary pairs. | false | false | false | false | false | true | false | false | true | false | false | false | false | false | false | false | false | false | 243,901 |
1906.08764 | Understanding More about Human and Machine Attention in Deep Neural
Networks | Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models encode attention mechanisms to focus on the most task-relevant parts of the input signal for further processing, which is called Machine/Neural/Artificial attention. Understanding the relation between human and machine attention is important for interpreting and designing neural networks. Many works claim that the attention mechanism offers an extra dimension of interpretability by explaining where the neural networks look. However, recent studies demonstrate that artificial attention maps do not always coincide with common intuition. In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design. With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms. Overall results demonstrate that human attention can benchmark the meaningful `ground-truth' in attention-driven tasks, where the more the artificial attention is close to human attention, the better the performance; for higher-level vision tasks, it is case-by-case. It would be advisable for attention-driven tasks to explicitly force a better alignment between artificial and human attention to boost the performance; such alignment would also improve the network explainability for higher-level computer vision tasks. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 135,961 |
2410.09250 | Quantum-Trained Convolutional Neural Network for Deepfake Audio
Detection | The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework's ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies. | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 497,504 |
2311.12820 | MSG-BART: Multi-granularity Scene Graph-Enhanced Encoder-Decoder
Language Model for Video-grounded Dialogue Generation | Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos. However, existing large visual-language models are not effective due to their latent features and decoder-only structure, especially with respect to spatio-temporal relationship reasoning. In this paper, we propose a novel approach named MSG-BART, which enhances the integration of video information by incorporating a multi-granularity spatio-temporal scene graph into an encoder-decoder pre-trained language model. Specifically, we integrate the global and local scene graph into the encoder and decoder, respectively, to improve both overall perception and target reasoning capability. To further improve the information selection capability, we propose a multi-pointer network to facilitate selection between text and video. Extensive experiments are conducted on three video-grounded dialogue benchmarks, which show the significant superiority of the proposed MSG-BART compared to a range of state-of-the-art approaches. | false | false | false | false | true | false | false | false | true | false | false | true | false | false | false | false | false | false | 409,494 |
1909.00867 | Investigating the Relationship between Multi-Party Linguistic
Entrainment, Team Characteristics, and the Perception of Team Social Outcomes | Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation. We first developed new measures of multi-party entrainment on features describing linguistic style, and then examined the relationship between entrainment and team characteristics in terms of gender composition, team size, and diversity. Next, we predicted the perception of team social outcomes using multi-party linguistic entrainment and team characteristics with a hierarchical regression model. We found that teams with greater gender diversity had higher minimum convergence than teams with less gender diversity. Entrainment contributed significantly to predicting perceived team social outcomes both alone and controlling for team characteristics. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 143,736 |
2305.13562 | Understanding and Improving Optimization in Predictive Coding Networks | Backpropagation (BP), the standard learning algorithm for artificial neural networks, is often considered biologically implausible. In contrast, the standard learning algorithm for predictive coding (PC) models in neuroscience, known as the inference learning algorithm (IL), is a promising, bio-plausible alternative. However, several challenges and questions hinder IL's application to real-world problems. For example, IL is computationally demanding, and without memory-intensive optimizers like Adam, IL may converge to poor local minima. Moreover, although IL can reduce loss more quickly than BP, the reasons for these speedups or their robustness remains unclear. In this paper, we tackle these challenges by 1) altering the standard implementation of PC circuits to substantially reduce computation, 2) developing a novel optimizer that improves the convergence of IL without increasing memory usage, and 3) establishing theoretical results that help elucidate the conditions under which IL is sensitive to second and higher-order information. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 366,555 |
1707.09445 | Joint CFO and Channel Estimation in Millimeter Wave Systems with One-Bit
ADCs | We develop a method to jointly estimate the carrier frequency offset (CFO) and the narrowband channel in millimeter wave (mmWave) MIMO systems operating with one-bit analog-to-digital converters (ADCs). We assume perfect timing synchronization and transform the underlying CFO-channel optimization problem to a higher dimensional space using lifting techniques. Exploiting the sparsity of mmWave MIMO channels in the angle domain, we perform joint estimation by solving a noisy quantized compressed sensing problem of the lifted version, using generalized approximate message passing. Simulation results show that our method is able to recover both the channel and the CFO using one-bit measurements. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 78,006 |
2210.03329 | Calibrating Factual Knowledge in Pretrained Language Models | Previous literature has proved that Pretrained Language Models (PLMs) can store factual knowledge. However, we find that facts stored in the PLMs are not always correct. It motivates us to explore a fundamental question: How do we calibrate factual knowledge in PLMs without re-training from scratch? In this work, we propose a simple and lightweight method CaliNet to achieve this goal. To be specific, we first detect whether PLMs can learn the right facts via a contrastive score between right and fake facts. If not, we then use a lightweight method to add and adapt new parameters to specific factual texts. Experiments on the knowledge probing task show the calibration effectiveness and efficiency. In addition, through closed-book question answering, we find that the calibrated PLM possesses knowledge generalization ability after fine-tuning. Beyond the calibration performance, we further investigate and visualize the knowledge calibration mechanism. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 321,992 |
2001.06826 | Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement | The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed. Code and model will be available at https://github.com/Li-Chongyi/Zero-DCE. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 160,898 |
2501.09509 | Power-Efficient RAN Intelligent Controllers Through Optimized KPI
Monitoring | The Open Radio Access Network (RAN) paradigm envisions a more flexible, interoperable, and intelligent RAN ecosystem via new open interfaces and elements like the RAN Intelligent Controller (RIC). However, the impact of these elements on Open RAN's power consumption remains heavily unexplored. This work for the first time evaluates the impact of Key Performance Indicator (KPI) monitoring on RIC's power consumption using real traffic and power measurements. By analyzing various RIC-RAN communication scenarios, we identify that RIC's power consumption can become a scalability bottleneck, particularly in large-scale deployments, even when RIC is limited to its core operational functionalities and without incorporating application-specific processes. In this context, also for the first time we explore potential power savings through the elimination of redundant KPI transmissions, extending existing techniques for identical subscription removal and KPI selection, achieving significant power consumption gains exceeding 87\% of the overall RIC power consumption. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 525,168 |
2310.19697 | A nonlinear spectral core-periphery detection method for multiplex
networks | Core-periphery detection aims to separate the nodes of a complex network into two subsets: a core that is densely connected to the entire network and a periphery that is densely connected to the core but sparsely connected internally. The definition of core-periphery structure in multiplex networks that record different types of interactions between the same set of nodes on different layers is nontrivial since a node may belong to the core in some layers and to the periphery in others. We propose a nonlinear spectral method for multiplex networks that simultaneously optimises a node and a layer coreness vector by maximising a suitable nonconvex homogeneous objective function by a provably convergent alternating fixed point iteration. We derive a quantitative measure for the quality of a given multiplex core-periphery structure that allows the determination of the optimal core size. Numerical experiments on synthetic and real-world networks illustrate that our approach is robust against noisy layers and significantly outperforms baseline methods while improving the latter with our novel optimised layer coreness weights. As the runtime of our method depends linearly on the number of edges of the network it is scalable to large-scale multiplex networks. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | true | 404,097 |
2202.08174 | Towards Battery-Free Machine Learning and Inference in Underwater
Environments | This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction. To answer this question, we explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater battery-free inference and machine learning ubiquitous. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 280,788 |
2111.06027 | Theoretical Exploration of Flexible Transmitter Model | Neural network models generally involve two important components, i.e., network architecture and neuron model. Although there are abundant studies about network architectures, only a few neuron models have been developed, such as the MP neuron model developed in 1943 and the spiking neuron model developed in the 1950s. Recently, a new bio-plausible neuron model, Flexible Transmitter (FT) model, has been proposed. It exhibits promising behaviors, particularly on temporal-spatial signals, even when simply embedded into the common feedforward network architecture. This paper attempts to understand the properties of the FT network (FTNet) theoretically. Under mild assumptions, we show that: i) FTNet is a universal approximator; ii) the approximation complexity of FTNet can be exponentially smaller than those of commonly-used real-valued neural networks with feedforward/recurrent architectures and is of the same order in the worst case; iii) any local minimum of FTNet is the global minimum, implying that it is possible to identify global minima by local search algorithms. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 265,962 |
2210.11049 | How Does a Deep Learning Model Architecture Impact Its Privacy? A
Comprehensive Study of Privacy Attacks on CNNs and Transformers | As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from convolutional neural networks (CNNs) to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks than CNNs. Additionally, we identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures. | false | false | false | false | true | false | true | false | false | false | false | false | true | false | false | false | false | false | 325,162 |
1203.0488 | Multi-Level Feature Descriptor for Robust Texture Classification via
Locality-Constrained Collaborative Strategy | This paper introduces a simple but highly efficient ensemble for robust texture classification, which can effectively deal with translation, scale and changes of significant viewpoint problems. The proposed method first inherits the spirit of spatial pyramid matching model (SPM), which is popular for encoding spatial distribution of local features, but in a flexible way, partitioning the original image into different levels and incorporating different overlapping patterns of each level. This flexible setup helps capture the informative features and produces sufficient local feature codes by some well-chosen aggregation statistics or pooling operations within each partitioned region, even when only a few sample images are available for training. Then each texture image is represented by several orderless feature codes and thereby all the training data form a reliable feature pond. Finally, to take full advantage of this feature pond, we develop a collaborative representation-based strategy with locality constraint (LC-CRC) for the final classification, and experimental results on three well-known public texture datasets demonstrate the proposed approach is very competitive and even outperforms several state-of-the-art methods. Particularly, when only a few samples of each category are available for training, our approach still achieves very high classification performance. | false | false | false | false | false | true | false | false | false | false | false | true | false | false | false | false | false | false | 14,693 |
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