Machine Learning
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Image Translation of Faces from Visible to Thermal Modalities using Auxiliary Domain and Sensor Labels
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Exact Acceleration of K-Means++ and K-Means||
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Multi-Task Graphs with Autoencoders
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Accounting for Variance in Machine Learning Benchmarks
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Research Reproducibility as a Survival Analysis
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Bringing UMAP Closer to the Speed of Light with GPU Acceleration
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Sampling Approach Matters: Active Learning for Robotic Language Acquisition
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Modeling Watershed Nutrient Concentrations with AutoML
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Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory,
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A New Burrows Wheeler Transform Markov Distance
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A Step Toward Quantifying Independently Reproducible Machine Learning Research
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PyLZJD: An Easy to Use Tool for Machine Learning
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Hash-Grams On Many-Cores and Skewed Distributions
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Connecting Lyapunov Control Theory to Adversarial Attacks
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A Manifold Alignment Approach to Grounded Language Learning
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Heterogeneous Relational Kernel Learning
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Barrage of Random Transforms for Adversarially Robust Defense
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Adversarial Attacks, Regression, and Numerical Stability Regularization
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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning
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Learning to Make Predictions on Graphs with Autoencoders
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Growing and Retaining AI Talent for the United States Government
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Neural Fingerprint Enhancement
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Engineering a Simplified 0-Bit Consistent Weighted Sampling
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Hash-Grams: Faster N-Gram Features for Classification and Malware Detection
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Linear Models with Many Cores: A Stochastic Atomic Update Scheme
Health and Responsible AI
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Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
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Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches
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An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
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Real-time estimation of disease activity in emerging outbreaks using internet search information
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Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams
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The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data
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COVID-19 Kaggle Literature Organization
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Exploratory Analysis of COVID-19 Tweets Using Topic Modeling, UMAP, and DiGraphs
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Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR
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Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR
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Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
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Early Outcomes and Complications of Obese Patients Undergoing Shoulder Arthroplasty: a Meta-Analysis
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Improved Automatic Pharmacovigilance: An Enhancement to the MedWatcher Social System for Monitoring Adverse Events
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Bias Amplification in Artificial intelligence Systems
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Gradient Reversal Against Discrimination
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Gradient Reversal Against Discrimination: A Fair Neural Network Learning Approach
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What About Applied Fairness?
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Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.
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Combining participatory influenza surveillance with modeling and forecasting: Three alternative approaches
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Social Media Listening for Routine Post-Marketing Safety Surveillance
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Sustained reduction of diversion and abuse after introduction of an abuse deterrent formulation of extended release oxycodone
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Diversion and Illicit Sale of Extended Release Tapentadol in the United States
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Increasing Patient Engagement in Pharmacovigilance Through Online Community Outreach and Mobile Reporting Applications: An Analysis of Adverse Event Reporting for the Essure Device in the US.
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Crowdsourcing black market prices for prescription opioids
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Dr. AI, Where Did You Get Your Degree?
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Dr. AI, Where Did You Get Your Degree?
Security and Privacy
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Adversarial Transfer Attacks With Unknown Data and Class Overlap
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A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels
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Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints
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Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
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A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
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Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors
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Automatic Yara Rule Generation Using Biclustering
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Would a File by Any Other Name Seem as Malicious?
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KiloGrams: Very Large N-Grams for Malware Classification
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Non-Negative Networks Against Adversarial Attacks
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Lempel-Ziv Jaccard Distance, an effective alternative to ssdeep and sdhash
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Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
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Malware Detection by Eating a Whole EXE
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An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance
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What can N-Grams Learn for Malware Detection?
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Malware Classification and Class Imbalance via Stochastic Hashed LZJD
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Learning the PE Header, Malware Detection with Minimal Domain Knowledge
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An investigation of byte n-gram features for malware classification
Quantum Computing
Progressing the field of quantum computing could bring about dramatic leaps in computing power. These increased processing speeds will catalyze revolutionary advancements in nearly every industry and discipline—communications, healthcare, materials engineering, manufacturing, finance, and national security all stand to gain.
Want to know what's ahead for quantum computing?
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