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Title: Machine Learning and Cosmology Abstract: Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educationa... |
Title: POETREE: Interpretable Policy Learning with Adaptive Decision Trees Abstract: Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation pe... |
Title: Graph filtering over expanding graphs Abstract: Our capacity to learn representations from data is related to our ability to design filters that can leverage their coupling with the underlying domain. Graph filters are one such tool for network data and have been used in a myriad of applications. But graph filte... |
Title: Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering Abstract: Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite signifi... |
Title: A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration Abstract: Determinantal points processes are a promising but relatively under-developed tool in machine learning and statistical modelling, being the canonical statistical example of distributions with rep... |
Title: Surrogate Gap Minimization Improves Sharpness-Aware Training Abstract: The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a \textit{perturbed loss} defined as the maximum loss within a neighborhood in the parameter space. However, we show that both sharp and flat minim... |
Title: Practical data monitoring in the internet-services domain Abstract: Large-scale monitoring, anomaly detection, and root cause analysis of metrics are essential requirements of the internet-services industry. To address the need to continuously monitor millions of metrics, many anomaly detection approaches are be... |
Title: Neural Solvers for Fast and Accurate Numerical Optimal Control Abstract: Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive b... |
Title: Can A Neural Network Hear the Shape of A Drum? Abstract: We have developed a deep neural network that reconstructs the shape of a polygonal domain given the first hundred of its Laplacian eigenvalues. Having an encoder-decoder structure, the network maps input spectra to a latent space and then predicts the disc... |
Title: Combining AI/ML and PHY Layer Rule Based Inference -- Some First Results Abstract: In 3GPP New Radio (NR) Release 18 we see the first study item starting in May 2022, which will evaluate the potential of AI/ML methods for Radio Access Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use... |
Title: Regenerative Particle Thompson Sampling Abstract: This paper proposes regenerative particle Thompson sampling (RPTS), a flexible variation of Thompson sampling. Thompson sampling itself is a Bayesian heuristic for solving stochastic bandit problems, but it is hard to implement in practice due to the intractabili... |
Title: On Suspicious Coincidences and Pointwise Mutual Information Abstract: Barlow (1985) hypothesized that the co-occurrence of two events $A$ and $B$ is "suspicious" if $P(A,B) \gg P(A) P(B)$. We first review classical measures of association for $2 \times 2$ contingency tables, including Yule's $Y$ (Yule, 1912), wh... |
Title: Does Corpus Quality Really Matter for Low-Resource Languages? Abstract: The vast majority of non-English corpora are derived from automatically filtered versions of CommonCrawl. While prior work has identified major issues on the quality of these datasets (Kreutzer et al., 2021), it is not clear how this impacts... |
Title: Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers Abstract: Training very deep neural networks is still an extremely challenging task. The common solution is to use shortcut connections and normalization layers, which are both crucial ingredients in the popular ResNet architecture. How... |
Title: Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective Abstract: We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility an... |
Title: One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning Abstract: The current literature on self-supervised learning (SSL) focuses on developing learning objectives to train neural networks more effectively on unlabeled data. The typical development process involves... |
Title: Privacy-Aware Compression for Federated Data Analysis Abstract: Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate aggregate statistics. Two major challenges in this framework are privacy... |
Title: CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images Abstract: Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of ... |
Title: Object Manipulation via Visual Target Localization Abstract: Object manipulation is a critical skill required for Embodied AI agents interacting with the world around them. Training agents to manipulate objects, poses many challenges. These include occlusion of the target object by the agent's arm, noisy object ... |
Title: HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model Abstract: One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital ... |
Title: DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies Abstract: Extreme pricing anomalies may occur unexpectedly without a trivial cause, and equity traders typically experience a meticulous process to source disparate information and analyze its reliability befor... |
Title: Learning Transient Partial Differential Equations with Local Neural Operators Abstract: In decades, enormous computational resources are poured into solving the transient partial differential equations for multifarious physical fields. The latest artificial intelligence has shown great potential in accelerating ... |
Title: Energy-Latency Attacks via Sponge Poisoning Abstract: Sponge examples are test-time inputs carefully-optimized to increase energy consumption and latency of neural networks when deployed on hardware accelerators. In this work, we demonstrate that sponge attacks can also be implanted at training time, when model ... |
Title: RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing Abstract: Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring devices and analyzing collected data. Machine learning methods are commonly utilized fo... |
Title: MoReL: Multi-omics Relational Learning Abstract: Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dea... |
Title: A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain Abstract: In the whole aircraft structural optimization loop, thermal analysis plays a very important role. But it faces a severe computational burden when directly applying traditional numeric... |
Title: Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning Abstract: The abundance of dark matter subhalos orbiting a host galaxy is a generic prediction of the cosmological framework. It is a promising way to constrain the nature of dark matter. Here we describe the challenges of ... |
Title: Towards understanding deep learning with the natural clustering prior Abstract: The prior knowledge (a.k.a. priors) integrated into the design of a machine learning system strongly influences its generalization abilities. In the specific context of deep learning, some of these priors are poorly understood as the... |
Title: SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence Abstract: Recent advances in wearable devices and Internet-of-Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging... |
Title: Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps Abstract: While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as many-to-many. That is, a single input can potentially yi... |
Title: UNet Architectures in Multiplanar Volumetric Segmentation -- Validated on Three Knee MRI Cohorts Abstract: UNet has become the gold standard method for segmenting 2D medical images that any new method must be validated against. However, in recent years, several variations of the seminal UNet have been proposed w... |
Title: Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling Abstract: Constitutive modeling based on continuum mechanics theory has been a classical approach for modeling the mechanical responses of materials. However, when constitutive laws are unknown or when de... |
Title: SocialVAE: Human Trajectory Prediction using Timewise Latents Abstract: Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncert... |
Title: A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks Abstract: The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learn... |
Title: AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning Abstract: Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. In addition, finding the optimal... |
Title: HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction Abstract: In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of under-sampled and noisy measurements. Deep learning approaches have been proven to be successful in solving this ill-pose... |
Title: Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos Abstract: In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision. ... |
Title: Zipfian environments for Reinforcement Learning Abstract: As humans and animals learn in the natural world, they encounter distributions of entities, situations and events that are far from uniform. Typically, a relatively small set of experiences are encountered frequently, while many important experiences occu... |
Title: Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them Abstract: False assumptions about sex and gender are deeply embedded in the medical system, including that they are binary, static, and concordant. Machine learning researchers must understand the nature of... |
Title: A Deep Dive into Dataset Imbalance and Bias in Face Identification Abstract: As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e.,... |
Title: Data Contamination: From Memorization to Exploitation Abstract: Pretrained language models are typically trained on massive web-based datasets, which are often "contaminated" with downstream test sets. It is not clear to what extent models exploit the contaminated data for downstream tasks. We present a principl... |
Title: Unified Visual Transformer Compression Abstract: Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive,... |
Title: Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction Abstract: Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data. In our EHRs, for example, the missing rates can be as high as 90% for some features, with an a... |
Title: Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling Abstract: Provably sample-efficient Reinforcement Learning (RL) with rich observations and function approximation has witnessed tremendous recent progress, particularly when the underlying ... |
Title: Neural RF SLAM for unsupervised positioning and mapping with channel state information Abstract: We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. T... |
Title: 2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips Abstract: The ever-growing volume of satellite imagery data presents a challenge for industry and governments making data-driven decisions based on the timely analysis of very large data sets. Commonly ... |
Title: Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning Abstract: Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement. In contrast, dual and multi arm robot platform... |
Title: Self-Distribution Distillation: Efficient Uncertainty Estimation Abstract: Deep learning is increasingly being applied in safety-critical domains. For these scenarios it is important to know the level of uncertainty in a model's prediction to ensure appropriate decisions are made by the system. Deep ensembles ar... |
Title: Improving Word Translation via Two-Stage Contrastive Learning Abstract: Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust and effective two-stage contrastive learning framework for the... |
Title: TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation Abstract: Real-time density estimation is ubiquitous in many applications, including computer vision and signal processing. Kernel density estimation is arguably one of the most commonly used density estimation techniques,... |
Title: ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data Abstract: Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application... |
Title: NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction Abstract: Datacenters execute large computational jobs, which are composed of smaller tasks. A job completes when all its tasks finish, so stragglers -- rare, yet extremely slow tasks -- are a major impediment to datacenter performance.... |
Title: Adaptive Noisy Matrix Completion Abstract: Low-rank matrix completion has been studied extensively under various type of categories. The problem could be categorized as noisy completion or exact completion, also active or passive completion algorithms. In this paper we focus on adaptive matrix completion with bo... |
Title: Domain Adaptive Hand Keypoint and Pixel Localization in the Wild Abstract: We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e.g., outdoors) when we only have labeled images taken under very different conditions (e.g., indoors). In ... |
Title: Gradient Correction beyond Gradient Descent Abstract: The great success neural networks have achieved is inseparable from the application of gradient-descent (GD) algorithms. Based on GD, many variant algorithms have emerged to improve the GD optimization process. The gradient for back-propagation is apparently ... |
Title: A Multi-parameter Updating Fourier Online Gradient Descent Algorithm for Large-scale Nonlinear Classification Abstract: Large scale nonlinear classification is a challenging task in the field of support vector machine. Online random Fourier feature map algorithms are very important methods for dealing with large... |
Title: Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance Abstract: The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort to iterative search methods on the training se... |
Title: Dual Diffusion Implicit Bridges for Image-to-Image Translation Abstract: Common image-to-image translation methods rely on joint training over data from both source and target domains. This excludes cases where domain data is private (e.g., in a federated setting), and often means that a new model has to be trai... |
Title: Reducing Flipping Errors in Deep Neural Networks Abstract: Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically trained for many epochs and then a validation dataset is used to select the D... |
Title: COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks Abstract: As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks... |
Title: How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies Abstract: Autonomous driving has the potential to revolutionize mobility and is hence an active area of research. In practice, the behavior of autonomous vehicles must be acceptable, i.e., efficient, sa... |
Title: FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction Abstract: Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practi... |
Title: CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning Abstract: Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has become one of the most widely ... |
Title: Unsupervised Semantic Segmentation by Distilling Feature Correspondences Abstract: Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce features for every pixel that are... |
Title: On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models Abstract: Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on le... |
Title: Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling Abstract: Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries, among other applications. We demo... |
Title: Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks Abstract: This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reu... |
Title: Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents Abstract: Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap is that the simulated systems alw... |
Title: Learning Audio Representations with MLPs Abstract: In this paper, we propose an efficient MLP-based approach for learning audio representations, namely timestamp and scene-level audio embeddings. We use an encoder consisting of sequentially stacked gated MLP blocks, which accept 2D MFCCs as inputs. In addition, ... |
Title: Deepchecks: A Library for Testing and Validating Machine Learning Models and Data Abstract: This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues,... |
Title: Resilient Neural Forecasting Systems Abstract: Industrial machine learning systems face data challenges that are often under-explored in the academic literature. Common data challenges are data distribution shifts, missing values and anomalies. In this paper, we discuss data challenges and solutions in the conte... |
Title: Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations Abstract: We introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional pa... |
Title: Differentiable DAG Sampling Abstract: We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges c... |
Title: Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act Abstract: Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we drift from default, instinctive or memorized b... |
Title: Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation Abstract: Using huge training datasets can be costly and inconvenient. This article explores various data distillation techniques that can reduce the amount of data required to successfully train deep networks. Insp... |
Title: An elementary analysis of ridge regression with random design Abstract: In this note, we provide an elementary analysis of the prediction error of ridge regression with random design. The proof is short and self-contained. In particular, it bypasses the use of Rudelson's deviation inequality for covariance matri... |
Title: Undersmoothing Causal Estimators with Generative Trees Abstract: Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions. It is, however, hard to infer these effects from observational data. One major problem that can arise is covariate shift where t... |
Title: MIMO-GAN: Generative MIMO Channel Modeling Abstract: We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and easy to s... |
Title: Less is More: Summary of Long Instructions is Better for Program Synthesis Abstract: Despite the success of large pre-trained language models (LMs) such as Codex, they show below-par performance on the larger and more complicated programming related questions. We show that LMs benefit from the summarized version... |
Title: Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks Abstract: Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even wh... |
Title: Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption Abstract: This work presents an extended version of the Vehicle Energy Dataset (VED), which is a openly released large-scale dataset for vehicle energy consumption analysis. Compared with ... |
Title: Adversarial Learned Fair Representations using Dampening and Stacking Abstract: As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data i... |
Title: Context-Aware Drift Detection Abstract: When monitoring machine learning systems, two-sample tests of homogeneity form the foundation upon which existing approaches to drift detection build. They are used to test for evidence that the distribution underlying recent deployment data differs from that underlying th... |
Title: The Structured Abstain Problem and the Lov\'asz Hinge Abstract: The Lov\'asz hinge is a convex surrogate recently proposed for structured binary classification, in which $k$ binary predictions are made simultaneously and the error is judged by a submodular set function. Despite its wide usage in image segmentati... |
Title: Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface Abstract: Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprostheti... |
Title: Counterfactual Inference of Second Opinions Abstract: Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide when and from whom to seek a second opinion. In this paper, we look at the design ... |
Title: Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics Abstract: In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most of the existing deep ... |
Title: Learning Representation for Bayesian Optimization with Collision-free Regularization Abstract: Bayesian optimization has been challenged by datasets with large-scale, high-dimensional, and non-stationary characteristics, which are common in real-world scenarios. Recent works attempt to handle such input by apply... |
Title: Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction Abstract: Non-line-of-sight reconstruction (NLoS) is a novel indirect imaging modality that aims to recover objects or scene parts outside the field of view from measurements of light that is indirectly scattered off a dire... |
Title: MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients Abstract: Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that... |
Title: Measuring Fairness of Text Classifiers via Prediction Sensitivity Abstract: With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of c... |
Title: High dimensional change-point detection: a complete graph approach Abstract: The aim of online change-point detection is for a accurate, timely discovery of structural breaks. As data dimension outgrows the number of data in observation, online detection becomes challenging. Existing methods typically test only ... |
Title: Multiscale Sensor Fusion and Continuous Control with Neural CDEs Abstract: Though robot learning is often formulated in terms of discrete-time Markov decision processes (MDPs), physical robots require near-continuous multiscale feedback control. Machines operate on multiple asynchronous sensing modalities, each ... |
Title: Relational Self-Supervised Learning Abstract: Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (\ie, the different augmented ima... |
Title: Attacking deep networks with surrogate-based adversarial black-box methods is easy Abstract: A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existing approaches of this type underperform ... |
Title: Tangles and Hierarchical Clustering Abstract: We establish a connection between tangles, a concept from structural graph theory that plays a central role in Robertson and Seymour's graph minor project, and hierarchical clustering. Tangles cannot only be defined for graphs, but in fact for arbitrary connectivity ... |
Title: Learning Where To Look -- Generative NAS is Surprisingly Efficient Abstract: The efficient, automated search for well-performing neural architectures (NAS) has drawn increasing attention in the recent past. Thereby, the predominant research objective is to reduce the necessity of costly evaluations of neural arc... |
Title: Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey Abstract: Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond human accuracy in many tasks, but at the cos... |
Title: What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study Abstract: Although many fields have witnessed the superior performance brought about by deep learning, the robustness of neural networks remains an open issue. Specifically, a small adversarial perturbation on the input may cause t... |
Title: Practical Conditional Neural Processes Via Tractable Dependent Predictions Abstract: Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs ... |
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