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Title: AL-PINNs: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks Abstract: Physics-Informed Neural Networks (PINNs) has become a prominent application of deep learning in scientific computation, as it is a powerful approximator of solutions to nonlinear partial differential equations (PDEs).... |
Title: Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization Abstract: Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective. In this letter, we have developed a hybrid materials informatics ap... |
Title: Meta Transfer Learning for Early Success Prediction in MOOCs Abstract: Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left b... |
Title: OPT: Open Pre-trained Transformer Language Models Abstract: Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant cap... |
Title: Deep Learning: From Basics to Building Deep Neural Networks with Python Abstract: This book is intended for beginners who have no familiarity with deep learning. Our only expectation from readers is that they already have the basic programming skills in Python. |
Title: The Equity Framework: Fairness Beyond Equalized Predictive Outcomes Abstract: Machine Learning (ML) decision-making algorithms are now widely used in predictive decision-making, for example, to determine who to admit and give a loan. Their wide usage and consequential effects on individuals led the ML community ... |
Title: Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques Abstract: Developing a rapid, but also reliable and efficient, method for classifying the seismic damage potential of buildings constructed in countries with regions of high seismicity is always at the forefr... |
Title: A Probabilistic Interpretation of Transformers Abstract: We propose a probabilistic interpretation of exponential dot product attention of transformers and contrastive learning based off of exponential families. The attention sublayer of transformers is equivalent to a gradient ascent step of the log normalizer,... |
Title: Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review Abstract: The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data ana... |
Title: Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages Abstract: We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervise... |
Title: Ensemble pruning via an integer programming approach with diversity constraints Abstract: Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classifier... |
Title: ComPhy: Compositional Physical Reasoning of Objects and Events from Videos Abstract: Objects' motions in nature are governed by complex interactions and their properties. While some properties, such as shape and material, can be identified via the object's visual appearances, others like mass and electric charge... |
Title: A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction Abstract: More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are know... |
Title: Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation Abstract: Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context information to decrease the ambiguity o... |
Title: D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction Abstract: The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Co... |
Title: Transformers in Time-series Analysis: A Tutorial Abstract: Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects of time-series analysis. This tutorial provides an overview of the Transfor... |
Title: Emotion-Controllable Generalized Talking Face Generation Abstract: Despite the significant progress in recent years, very few of the AI-based talking face generation methods attempt to render natural emotions. Moreover, the scope of the methods is majorly limited to the characteristics of the training dataset, h... |
Title: SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels Abstract: Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-ensemble label correction (SELC) to progressively co... |
Title: Reproducing Kernels and New Approaches in Compositional Data Analysis Abstract: Compositional data, such as human gut microbiomes, consist of non-negative variables whose only the relative values to other variables are available. Analyzing compositional data such as human gut microbiomes needs a careful treatmen... |
Title: VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation Abstract: Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed sty... |
Title: Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data Abstract: With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have been known to utilize static real estate data (e... |
Title: Performance Weighting for Robust Federated Learning Against Corrupted Sources Abstract: Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models while offering participating clients certain pr... |
Title: Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis Abstract: Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors... |
Title: Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language Abstract: Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by test... |
Title: Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems Abstract: Innovation in the physical layer of communication systems has traditionally been achieved by breaking down the transceivers into sets of processing blocks, each optimized independently based on mathematical models. C... |
Title: Streaming Inference for Infinite Non-Stationary Clustering Abstract: Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, st... |
Title: An improvement to a result about graph isomorphism networks using the prime factorization theorem Abstract: The unique prime factorization theorem is used to show the existence of a function on a countable set $\mathcal{X}$ so that the sum aggregator function is injective on all multisets of $\mathcal{X}$ of fin... |
Title: Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations Abstract: We investigate uncertainty estimation and multimodality via the non-deterministic predictions of Bayesian neural networks (BNNs) in fluid simulations. To this end, we deploy BNNs in three challenging experimental test-c... |
Title: FINETUNA: Fine-tuning Accelerated Molecular Simulations Abstract: Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world ... |
Title: COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence Abstract: Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multiv... |
Title: Adversarial attacks on an optical neural network Abstract: Adversarial attacks have been extensively investigated for machine learning systems including deep learning in the digital domain. However, the adversarial attacks on optical neural networks (ONN) have been seldom considered previously. In this work, we ... |
Title: Retrieval-Enhanced Machine Learning Abstract: Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core princ... |
Title: One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model Abstract: Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most ... |
Title: Triangular Dropout: Variable Network Width without Retraining Abstract: One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution. This latter property is often exploited when introducing informa... |
Title: Scheduling with Speed Predictions Abstract: Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve... |
Title: Norm-Agnostic Linear Bandits Abstract: Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the reward generation. Such an assumption forces... |
Title: From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks Abstract: Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle wh... |
Title: Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction Abstract: Prediction of network traffic behavior is significant for the effective management of modern telecommunication networks. However, the intuitive approach of predicting network traffic using administrative experience and market anal... |
Title: Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods Abstract: This paper is dedicated to Prof. Eduardo Sontag on the occasion of his seventieth birthday. In this paper, we build upon the ideas first proposed in Gladyshev (1965) to develop a very general framework for proving the ... |
Title: CANShield: Signal-based Intrusion Detection for Controller Area Networks Abstract: Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control. However, with the expansion of advanced connectivity features in automobiles ... |
Title: FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning Abstract: Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is far more complex and challenging owing to varying... |
Title: Distilling Governing Laws and Source Input for Dynamical Systems from Videos Abstract: Distilling interpretable physical laws from videos has led to expanded interest in the computer vision community recently thanks to the advances in deep learning, but still remains a great challenge. This paper introduces an e... |
Title: Open vs Closed-ended questions in attitudinal surveys -- comparing, combining, and interpreting using natural language processing Abstract: To improve the traveling experience, researchers have been analyzing the role of attitudes in travel behavior modeling. Although most researchers use closed-ended surveys, t... |
Title: Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies Abstract: Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and prop... |
Title: Predicting Issue Types with seBERT Abstract: Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the... |
Title: Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks Abstract: We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates. Given a garment, we generate a simulation database and extract virtual b... |
Title: Learning Label Initialization for Time-Dependent Harmonic Extension Abstract: Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version... |
Title: TracInAD: Measuring Influence for Anomaly Detection Abstract: As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies bas... |
Title: Finding patterns in Knowledge Attribution for Transformers Abstract: We analyze the Knowledge Neurons framework for the attribution of factual and relational knowledge to particular neurons in the transformer network. We use a 12-layer multi-lingual BERT model for our experiments. Our study reveals various inter... |
Title: Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review Abstract: With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researche... |
Title: Smooth over-parameterized solvers for non-smooth structured optimization Abstract: Non-smooth optimization is a core ingredient of many imaging or machine learning pipelines. Non-smoothness encodes structural constraints on the solutions, such as sparsity, group sparsity, low-rank and sharp edges. It is also the... |
Title: Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP) Abstract: Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since these image-text models differ from pr... |
Title: Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? Abstract: Several popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explor... |
Title: Multimodal Detection of Unknown Objects on Roads for Autonomous Driving Abstract: Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized... |
Title: An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles Abstract: Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite... |
Title: A Falsificationist Account of Artificial Neural Networks Abstract: Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and inductio... |
Title: Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration Abstract: Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the ... |
Title: ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection Abstract: As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy... |
Title: RLFlow: Optimising Neural Network Subgraph Transformation with World Models Abstract: Training deep learning models takes an extremely long execution time and consumes large amounts of computing resources. At the same time, recent research proposed systems and compilers that are expected to decrease deep learnin... |
Title: Efficient and Convergent Federated Learning Abstract: Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To address these issues, this paper proposes ... |
Title: Learning Coulomb Diamonds in Large Quantum Dot Arrays Abstract: We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device u... |
Title: High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation Abstract: We study the first gradient descent step on the first-layer parameters $\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) = \frac{1}{\sqrt{N}}\boldsymbol{a}^\top\sigma(\boldsymbol{W}^\top... |
Title: Efficient implementation of incremental proximal-point methods Abstract: Model training algorithms which observe a small portion of the training set in each computational step are ubiquitous in practical machine learning, and include both stochastic and online optimization methods. In the vast majority of cases,... |
Title: On the Convergence of Fictitious Play: A Decomposition Approach Abstract: Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable c... |
Title: Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates Abstract: In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) to update the global model while protecting their data priv... |
Title: Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting Abstract: Traffic flow forecasting is essential for traffic planning, control and management. The main challenge of traffic forecasting tasks is accurately capturing traffic networks' spatial and temporal correlation. Althoug... |
Title: Scalable Regularised Joint Mixture Models Abstract: In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the heterogeneity can affect both predictive performance and interpretability. Buildi... |
Title: Subspace Diffusion Generative Models Abstract: Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restri... |
Title: A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE) Abstract: We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framewo... |
Title: On the uncertainty principle of neural networks Abstract: Despite the successes in many fields, it is found that neural networks are vulnerability and difficult to be both accurate and robust (robust means that the prediction of the trained network stays unchanged for inputs with non-random perturbations introdu... |
Title: Meta Learning for Natural Language Processing: A Survey Abstract: Deep learning has been the mainstream technique in natural language processing (NLP) area. However, the techniques require many labeled data and are less generalizable across domains. Meta-learning is an arising field in machine learning studying ... |
Title: Compact Neural Networks via Stacking Designed Basic Units Abstract: Unstructured pruning has the limitation of dealing with the sparse and irregular weights. By contrast, structured pruning can help eliminate this drawback but it requires complex criterion to determine which components to be pruned. To this end,... |
Title: ExSpliNet: An interpretable and expressive spline-based neural network Abstract: In this paper we present ExSpliNet, an interpretable and expressive neural network model. The model combines ideas of Kolmogorov neural networks, ensembles of probabilistic trees, and multivariate B-spline representations. We give a... |
Title: Fair Feature Subset Selection using Multiobjective Genetic Algorithm Abstract: The feature subset selection problem aims at selecting the relevant subset of features to improve the performance of a Machine Learning (ML) algorithm on training data. Some features in data can be inherently noisy, costly to compute,... |
Title: BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Abstract: Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework... |
Title: Efficient Fine-Tuning of BERT Models on the Edge Abstract: Resource-constrained devices are increasingly the deployment targets of machine learning applications. Static models, however, do not always suffice for dynamic environments. On-device training of models allows for quick adaptability to new scenarios. Wi... |
Title: Privacy Amplification via Random Participation in Federated Learning Abstract: Running a randomized algorithm on a subsampled dataset instead of the entire dataset amplifies differential privacy guarantees. In this work, in a federated setting, we consider random participation of the clients in addition to subsa... |
Title: RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer Abstract: Learning scene flow from a monocular camera still remains a challenging task due to its ill-posedness as well as lack of annotated data. Self-supervised methods demonstrate learning scene flow estimation from unlabeled data, yet t... |
Title: PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting Abstract: Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of smal... |
Title: RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds Abstract: Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of differen... |
Title: A Real Time 1280x720 Object Detection Chip With 585MB/s Memory Traffic Abstract: Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints, this paper proposes a low memory traffic DLA chip ... |
Title: An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model Abstract: Real-world IP network traffic is susceptible to external and internal factors such as new internet service integration, traffic migration, internet application, etc. Due to these factors, the actual internet traffic is no... |
Title: Conditional $\beta$-VAE for De Novo Molecular Generation Abstract: Deep learning has significantly advanced and accelerated de novo molecular generation. Generative networks, namely Variational Autoencoders (VAEs) can not only randomly generate new molecules, but also alter molecular structures to optimize speci... |
Title: Modeling and Correcting Bias in Sequential Evaluation Abstract: We consider the problem of sequential evaluation, in which an evaluator observes candidates in a sequence and assigns scores to these candidates in an online, irrevocable fashion. Motivated by the psychology literature that has studied sequential bi... |
Title: Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction Abstract: Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms and has been applied to many machine learning tasks such as data cleaning, few-shot learning, and neural architecture search. ... |
Title: Automatic Segmentation of Aircraft Dents in Point Clouds Abstract: Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are b... |
Title: Toward Robust Spiking Neural Network Against Adversarial Perturbation Abstract: As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical applications, the security concerns in SNNs attract more attention. Currently, researchers have already demonstrated an SNN can be attacked... |
Title: Automated Learning of Interpretable Models with Quantified Uncertainty Abstract: Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently ... |
Title: AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning Abstract: WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and... |
Title: Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment Abstract: The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and ... |
Title: Adversarial Training for High-Stakes Reliability Abstract: In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes settings is adversarial training, which uses an adversary to generate examples ... |
Title: The scope for AI-augmented interpretation of building blueprints in commercial and industrial property insurance Abstract: This report, commissioned by the WTW research network, investigates the use of AI in property risk assessment. It (i) reviews existing work on risk assessment in commercial and industrial pr... |
Title: Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize Abstract: This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive ... |
Title: A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis Abstract: Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades co... |
Title: ASTROMER: A transformer-based embedding for the representation of light curves Abstract: Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was trained on millions of MACHO R-band samples, and it can be easily fi... |
Title: Growing Isotropic Neural Cellular Automata Abstract: Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge of developmental biology. Recently, the Neural Cellular Automata (NCA) model was... |
Title: Deep Sequence Modeling for Anomalous ISP Traffic Prediction Abstract: Internet traffic in the real world is susceptible to various external and internal factors which may abruptly change the normal traffic flow. Those unexpected changes are considered outliers in traffic. However, deep sequence models have been ... |
Title: On Circuit Depth Scaling For Quantum Approximate Optimization Abstract: Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning. An... |
Title: MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators Abstract: Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators. However, computat... |
Title: Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks Abstract: Deep learning (DL) is being used extensively for text classification. However, researchers have demonstrated the vulnerability of such classifiers to adversarial attacks. Attackers m... |
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