Search Results for author:
Found 11 papers, 4 papers with code
LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature
The results suggest that LLMs do not produce good knowledge entities that reflect the cybersecurity context, but our results show some potential for noun extractors.
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning
In this paper, we show that a class of evolutionary algorithms (EAs) inspired by the Gillespie-Orr Mutational Landscapes model for natural evolution is formally equivalent to SGD in certain settings and, in practice, is well adapted to large ANNs.
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
We then leverage this definition to show that a general class of gradient-free ML algorithms - ($1,\lambda$)-Evolutionary Search - can be combined with classical distributed consensus algorithms to generate gradient-free byzantine-resilient distributed learning algorithms.
Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs
This prominence amplified prior concerns regarding the misuse of LLMs and led to the emergence of numerous tools to detect LLMs in the wild.
Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models).
Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale
The advent of the internet, followed shortly by the social media made it ubiquitous in consuming and sharing information between anyone with access to it.
Evolutionary perspective on model fine-tuning
Be it in natural language generation or in the image generation, massive performances gains have been achieved in the last years.
Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?
In this paper we address the problem of fine-tuned text generation with a limited computational budget.
On the Transcriptomic Signature and General Stress State Associated with Aneuploidy
Whether aneuploid cells with diverse karyotypes have any properties in common has a been a subject of intense interest.
Host-Pathongen Co-evolution Inspired Algorithm Enables Robust GAN Training
Generative adversarial networks (GANs) are pairs of artificial neural networks that are trained one against each other.
The Probabilistic Fault Tolerance of Neural Networks in the Continuous Limit
We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.
