Search Results for author:
Found 46 papers, 12 papers with code
Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements
We first evaluate the performance of LLMs, including GPT-4o and Llama 3. 1-405B, using the state-of-the-art PentestGPT tool.
Secure Multiparty Generative AI
In our research, we present a secure and private methodology for generative artificial intelligence that does not expose sensitive data or models to third-party AI providers.
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently explored.
The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
Point-of-Care Ultrasound (POCUS) is the practice of clinicians conducting and interpreting ultrasound scans right at the patient's bedside.
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors.
Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure
Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions.
Event-to-Video Conversion for Overhead Object Detection
In this paper, we investigate the viability of event streams for overhead object detection.
Nevermind: Instruction Override and Moderation in Large Language Models
Given the impressive capabilities of recent Large Language Models (LLMs), we investigate and benchmark the most popular proprietary and different sized open source models on the task of explicit instruction following in conflicting situations, e. g. overrides.
Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
In Natural Language Processing (NLP), the Elo rating system, originally designed for ranking players in dynamic games such as chess, is increasingly being used to evaluate Large Language Models (LLMs) through "A vs B" paired comparisons.
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation
Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately than traditional automated metrics.
Semantic Adversarial Attacks via Diffusion Models
Then there are two variants of this framework: 1) the Semantic Transformation (ST) approach fine-tunes the latent space of the generated image and/or the diffusion model itself; 2) the Latent Masking (LM) approach masks the latent space with another target image and local backpropagation-based interpretation methods.
DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models
Generating high-quality labeled image datasets is crucial for training accurate and robust machine learning models in the field of computer vision.
Lessons in Reproducibility: Insights from NLP Studies in Materials Science
In summary, our study appreciates the benchmark set by these seminal papers while advocating for further enhancements in research reproducibility practices in the field of NLP for materials science.
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data.
MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples
Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside.
Dictionary Learning with Accumulator Neurons
The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor.
EvoSTS Forecasting: Evolutionary Sparse Time-Series Forecasting
In each generation of our evolutionary algorithm, a set number of children with the same initial weights are spawned.
Perception Over Time: Temporal Dynamics for Robust Image Understanding
Next, we demonstrate how our novel visual perception framework can utilize this information "over time" using a biologically plausible algorithm with recurrent units, and as a result, significantly improving its accuracy and robustness over standard CNNs.
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety.
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Hence, it is important that these prediction models are extensively tested in various test scenarios involving interactive behaviors prior to deployment.
Multi-Agent Algorithmic Recourse
The recent adoption of machine learning as a tool in real world decision making has spurred interest in understanding how these decisions are being made.
Addressing the IEEE AV Test Challenge with Scenic and VerifAI
Sampling from an abstract scenario yields many different concrete scenarios which can be run as test cases for the AV.
Parallel and Multi-Objective Falsification with Scenic and VerifAI
Falsification has emerged as an important tool for simulation-based verification of autonomous systems.
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football(GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities.
ATRAS: Adversarially Trained Robust Architecture Search
In this paper, we explore the effect of architecture completeness on adversarial robustness.
Automatic Dynamic Parallelotope Bundles for Reachability Analysis of Nonlinear Systems
To this end, we investigate two techniques for generating the template directions.
Functional Protein Structure Annotation Using a Deep Convolutional Generative Adversarial Network
Identifying novel functional protein structures is at the heart of molecular engineering and molecular biology, requiring an often computationally exhaustive search.
Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks
We compare both strategies to buying and holding one single share for the period that we picked as a benchmark.
Robust SleepNets
In this study, we investigate eye closedness detection to prevent vehicle accidents related to driver disengagements and driver drowsiness.
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
Architecture size for a neural network contributes significantly to the success of any neural network.
Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures
In this paper, we study compact neural network architectures for binary classification and investigate improvements in speed and accuracy when favoring overcomplete architecture candidates that have a very high-dimensional representation of the input.
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation.
The Interpretable Dictionary in Sparse Coding
Artificial neural networks (ANNs), specifically deep learning networks, have often been labeled as black boxes due to the fact that the internal representation of the data is not easily interpretable.
The Selectivity and Competition of the Mind's Eye in Visual Perception
Research has shown that neurons within the brain are selective to certain stimuli.
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors
Our approach is semantic in that it employs a high-level representation of the distribution of environment scenarios that the detector is intended to work on.
Modeling Biological Immunity to Adversarial Examples
These exploits, or adversarial examples, are a type of signal attack that can change the output class of a classifier by perturbing the stimulus signal by an imperceptible amount.
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world.
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
It is programmatic in that scenario representation is a program in a domain-specific probabilistic programming language which can be used to generate synthetic data to test a given perception module.
Machine-learned metrics for predicting the likelihood of success in materials discovery
Materials discovery is often compared to the challenge of finding a needle in a haystack.
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text.
VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery.
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drastically change the output classification.
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
The brain does not work solely in a feed-forward fashion, but rather all of the neurons are in competition with each other; neurons are integrating information in a bottom up and top down fashion and incorporating expectation and feedback in the modeling process.
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
