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Title: Optimizing Mixture of Experts using Dynamic Recompilations Abstract: The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs). However, current DNN frameworks cannot effectively support the dynamic data flow ... |
Title: AmbiPun: Generating Humorous Puns with Ambiguous Context Abstract: In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. ... |
Title: Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features Abstract: Poetry generation, and creative language generation in general, usually suffers from the lack of large training data. In this paper, we present a novel framework to generate sonnets that does not require training on poems... |
Title: i-Code: An Integrative and Composable Multimodal Learning Framework Abstract: Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-superv... |
Title: Diverse Image Captioning with Grounded Style Abstract: Stylized image captioning as presented in prior work aims to generate captions that reflect characteristics beyond a factual description of the scene composition, such as sentiments. Such prior work relies on given sentiment identifiers, which are used to ex... |
Title: Assessing Dataset Bias in Computer Vision Abstract: A biased dataset is a dataset that generally has attributes with an uneven class distribution. These biases have the tendency to propagate to the models that train on them, often leading to a poor performance in the minority class. In this project, we will expl... |
Title: Frequency Domain-Based Detection of Generated Audio Abstract: Attackers may manipulate audio with the intent of presenting falsified reports, changing an opinion of a public figure, and winning influence and power. The prevalence of inauthentic multimedia continues to rise, so it is imperative to develop a set o... |
Title: Splicing Detection and Localization In Satellite Imagery Using Conditional GANs Abstract: The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distorti... |
Title: Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendations Abstract: Recommendations Systems have been identified to be one of the integral elements of driving sales in e-commerce sites. The utilization of opinion mining data extracted from trends has been attempted to imp... |
Title: Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis Abstract: Synthesized speech is common today due to the prevalence of virtual assistants, easy-to-use tools for generating and modifying speech signals, and remote work practices. Synthesized speech can also be used for nefar... |
Title: Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars Abstract: This paper considers meta-cognitive radars in an adversarial setting. A cognitive radar optimally adapts its waveform (response) in response to maneuvers (probes) of a possibly adversarial moving target. A meta-cogn... |
Title: Do More Negative Samples Necessarily Hurt in Contrastive Learning? Abstract: Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more "negative samples" in the contrastive loss improves downstream classification performance initially, beyond... |
Title: The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts Abstract: The ICML Expressive Vocalization (ExVo) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central... |
Title: Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency Abstract: There are many contexts where dyadic data is present. Social networking is a well-known example, where transparency has grown on importance. In these contexts, pairs of items are linked building a network where interactions pl... |
Title: Differentiable Simulation of Soft Multi-body Systems Abstract: We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynami... |
Title: XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction Abstract: Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and cau... |
Title: Bézier Curve Gaussian Processes Abstract: Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information. The predominant approach in this domain is given by neural networks, which incorporate either stochastic units or components. This p... |
Title: Self-focusing virtual screening with active design space pruning Abstract: High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model... |
Title: Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes Abstract: The exponential rise of social media networks has allowed the production, distribution, and consumption of data at a phenomenal rate. Moreover, the social media revolution has brought a unique phenomenon to social media platforms cal... |
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... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Explainable multi-class anomaly detection on functional data Abstract: In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest al... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach Abstract: Vacant parking space (VPS) prediction is one of the key issues of intelligent parking guidance systems. Accurately predicting VPS information plays a crucial role in intelligent parking guidance ... |
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: 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: 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: 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: 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: Time Shifts to Reduce the Size of Reservoir Computers Abstract: A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accur... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Understanding Urban Water Consumption using Remotely Sensed Data Abstract: Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine ... |
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: 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: 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: Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar Abstract: This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks.... |
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: 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: 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: Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis Abstract: EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG s... |
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: 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: 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: 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: Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus Abstract: With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most cur... |
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: 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: 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: 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: 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: The Limits of Word Level Differential Privacy Abstract: As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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... |
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