A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfo…
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48 results for “DL Models”
DLBricks automates DL benchmarking on CPUs, reducing effort and time.
problem Lack of representative and up-to-date DL benchmarks on CPUs.
method Decomposes DL models into runnable networks, leveraging layer repetition and auto-generation.
result Accurately estimates DL model performance and speeds up benchmarking time.
A review of ML and DL for ecological data analysis.
problem Understanding the strengths and limitations of ML and DL in ecological research.
method Historical overview, algorithm families, differences, universal principles, and emerging trends.
result ML and DL excel in prediction tasks but are still debated for causal inference.
A scalable DL benchmarking platform for evaluating and comparing models, frameworks, and hardware.
problem Lack of a uniform DL benchmarking platform for evaluating and comparing innovations.
method Identified 10 design features for a DL benchmarking platform, proposed MLModelScope, and implemented as an open-source project.
result Demonstrated how model, hardware, and framework selection affect accuracy and performance under different scenarios.
DL/FBF improves GPSR solutions by selecting compact, generalising expressions.
problem Overfitting and structural bloat in symbolic regression with genetic programming.
method Description length (DL) and fractional Bayes factor (FBF) criteria for selecting compact, generalising expressions.
result DL/FBF post-selection improves test performance compared to AIC/BIC baseline.
Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms have resulted in a plethora of design challenges related to the constraints introduced by the hardware…
Reliability is a critical consideration to DL-based systems. But the statistical nature of DL makes it quite vulnerable to invalid inputs, i.e., those cases that are not considered in the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to enhance the reli…
Benchmark for DL inference on embedded HWAs, focusing on autonomous driving.
problem Lack of comprehensive benchmarks for DL hardware.
method Developed a benchmark for inference on embedded HWAs, focusing on autonomous driving. Proposed new granularity, benchmark procedures, and performance indicators.
result Identifies mismatches between HWAs and DL models.
Improved DL models robust against adversarial attacks for wireless signal classification.
problem Adversarial attacks on deep learning-based wireless signal classifiers.
method Knowledge distillation and network pruning followed by adversarial training.
result Proposed models achieve better robustness and higher accuracy than standard models.
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
problem Solving inverse problems with indirect measurements and uncertainties.
method Bayesian Deep Learning, using neural networks and approximate computations.
result Effective solutions for inverse problems using Bayesian Deep Learning.
Develops deep probabilistic graphical modeling for better flexibility and interpretability.
problem Lack of flexibility in probabilistic graphical models and interpretability in deep learning.
method Combines deep learning and probabilistic graphical modeling to create flexible models with interpretable latent structures.
result Solves problems in probabilistic topic models and introduces new learning algorithms.
Paper improves candlestick classification model's resistance to adversarial attacks.
problem Adversarial attacks on deep learning models for candlestick classification.
method Constructs perturbed examples to enhance model robustness.
result Boosted stability of DL models for candlestick classification.
Deep learning improves automated detection of epileptic seizures.
problem Automated detection of epileptic seizures using traditional methods is limited.
method Deep learning techniques for feature extraction and classification.
result Deep learning enhances accuracy in diagnosing epileptic seizures.
Diestel-Leader graphs are neither hyperbolic nor CAT(0), so their visual boundaries may be pathological. Indeed, we show that for , carries the indiscrete topology. On the other hand, , while not Hausdorff, is , totally disconnected, and compact. Since $\text{D…
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them. This is exacerbated by the complicated procedures for evaluation. The lack of standard systems and efficient techniques for specifying and pro…
Survey of deep learning models in finance.
problem Improving financial models with deep learning.
method Categorized and analyzed financial applications of deep learning models.
result Outperformance of deep learning over classical models in finance.
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data…
Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffe…
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive …
Comparison of AutoML and DL shows AutoML outperforms simple DL for binary classification.
problem Choosing between AutoML and DL for binary classification tasks.
method Comparison of AutoML and DL on 6 public datasets, evaluation of a new AutoML tool.
result AutoML outperforms simple DL classifiers but integrating DL into AutoML improves performance further.
DL models can outperform regionalized models in hydrology by pooling diverse data.
problem Traditional wisdom in hydrology suggests regionalization improves model performance, but DL models can unify data for better performance.
method Used DL models on pooled data from different regions, showing improved performance compared to regionalized models.
result DL models can improve performance by pooling diverse data, highlighting the 'data synergy' effect.
Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models glo…
More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services. Efficient resource scheduling is essential for maximal utilization of expensive DL clusters. Existing cluster schedulers either are agnostic to ML workload characte…
Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. With the wide-spread usage of DL in critical and time-sensitive applications, including unmanned vehicles, drones, and video surveillance systems, onli…
Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we…
The increasing inclusion of Deep Learning (DL) models in safety-critical systems such as autonomous vehicles have led to the development of multiple model-based DL testing techniques. One common denominator of these testing techniques is the automated generation of test cases, e.g., new inputs transformed from the orig…
We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD…
A new method approximates deep neural networks using Kalman Filters.
problem Approximating deep neural networks with limited data.
method Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN).
result MEnKF-ANN can adequately approximate LSTM networks and provide uncertainty estimates.
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending these frameworks to accommodate the rapidly diversifying landscape of DL models and hardware platforms presents challenging tradeoffs betwee…
DL improves precipitation nowcasting from radar images.
problem Precise short-term precipitation predictions for extreme weather adaptation.
method Image-to-image translation using UNET CNN, compared to optical flow, persistence, and HRRR.
result UNET-based DL outperforms traditional models in 1 km x 1 km, 1 hour precipitation nowcasting.
DL models for MTS regression are vulnerable to adversarial attacks, posing risks in safety-critical applications.
problem Vulnerability of DL models to adversarial examples in MTS regression.
method Adversarial attack generation techniques from image classification were adapted for MTS.
result All state-of-the-art DL regression models (CNN, LSTM, GRU) are vulnerable to adversarial attacks.
New DL algorithm detects critical chest X-ray findings without manual annotations.
problem Lack of explainability and manual annotation costs for DL models in medical imaging.
method Multi-instance learning approach to jointly classify and localize critical findings in CXR.
result Competitive classification results on three CXR datasets.
Benanza speeds up DL model optimization by automatically generating micro-benchmarks and identifying inefficiencies.
problem Slow characterization/optimization cycles for DL models on GPUs.
method Benanza includes a model processor, benchmark generator, database, and analyzer.
result Benanza identifies optimizations in parallel layer execution, cuDNN, framework inefficiency, layer fusion, and Tensor Cores.
A new DL framework preserves geometric structures for causal predictions.
problem Designing deep learning models for geometrically structured data.
method Introduces a universal causal geometric DL framework.
result DL models can approximate any regular map between metric spaces.
Deep learning enhances smart fish farming through automated feature extraction.
problem Challenges in obtaining labeled data limit DL applications in aquaculture.
method Application of DL techniques in aquaculture, including live fish identification, species classification, and feeding decision-making.
result Deep learning's ability to automatically extract features is a significant contribution.
This paper clarifies deep learning for IS scholars.
problem Limited IS contributions in deep learning.
method Systematic review and framework development.
result Clear guidelines for IS scholars to make DL contributions.
Survey of integrating domain knowledge into DL models.
problem Improving DL model performance with limited data or complex functions.
method Five categories of approaches to inject domain knowledge into DL models.
result Survey identifies five main categories of approaches.
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges …
Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep le…
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusterscs.LG
Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batc…
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cs.LG
This review examines DL models for financial forecasting.
problem Lack of comprehensive reviews on DL for financial forecasting.
method Categorized studies by forecasting area and DL model type.
result DL models outperform traditional ML methods.
Defense against DL-based lithographic hotspot detectors backdooring attacks reduces success rate from 84% to ~0%
problem DL-based lithographic hotspot detectors are vulnerable to backdoor attacks that can misclassify hotspots.
method Training data augmentation to eliminate intentional biases introduced during training.
result Significant reduction in attack success rate (from 84% to ~0%) using the proposed defense.
This paper introduces a new metric for deep learning networks based on their classification performance.
problem The mystery and black-box nature of deep learning networks.
method Proposes a new distance measure based on the probabilistic performance of deep learning networks.
result The proposed metric space is compact and coincides with the quotient topological space.
Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for the efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD sy…
DeepPurpose simplifies DL for DTI prediction.
problem Accurate prediction of drug-target interactions.
method Comprehensive deep learning library with 15 compound and protein encoders and 50 neural architectures.
result State-of-the-art performance on benchmark datasets.
TNet combines DL with physics models to solve inverse problems efficiently.
problem Solving inverse problems with limited data and physics constraints.
method Model-constrained deep learning approach using TNet.
result TNet solutions are as accurate as traditional methods but faster.
Improving the efficacy of Deep Learning models for Heart Beat detection on heterogeneous datasetsstat.ML
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
problem Inefficiency of DL models on heterogeneous datasets for heart beat detection.
method Investigated and evaluated the use of Transfer Learning to adapt DL models to different datasets.
result Transfer Learning improves classification performance on small sample size datasets.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
problem Inferring surface/groundwater exchange from temperature data with high temporal resolution.
method Application of machine learning and deep learning algorithms to infer surface/groundwater exchange flux from subsurface temperature observations.
result DL methods outperform ML methods in interpreting noisy temperature data, especially with a smoothing filter.