8 citations · 21 across the 14 of their papers we have counts for
15 papers
Metalens-transparent ultrasound transducer module for extended depth photoacoustic microscopy
Krishnendu Samanta, Myunghoo Lee, Shubham Mirg +5
Optical-resolution photoacoustic microscopy (ORPAM) provides label-free optical-absorption contrast at millimeter-scale tissue depths with micrometer-scale lateral resolution. Howe…
Efficient Pre-Training of LLMs through Truncated SVD Representations
Kaivan Kamali, Kajetan Schweighofer, Hormoz Shahrzad +3
LLM pretraining is extremely costly; therefore, parameter-efficient LLM architectures have recently emerged as a compelling research direction. One such promising approach is to re…
Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models
Hormoz Shahrzad, Niharika Gajawelli, Kaitlin Maile +2
Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. St…
TerraLingua: Emergence and Analysis of Open-endedness in LLM Ecologies
Giuseppe Paolo, Jamieson Warner, Hormoz Shahrzad +3
As autonomous agents increasingly operate in real-world digital ecosystems, understanding how they coordinate, form institutions, and accumulate shared culture becomes both a scien…
Solving a Million-Step LLM Task with Zero Errors
Elliot Meyerson, Giuseppe Paolo, Roberto Dailey +6
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by h…
GPU-Accelerated Rule Evaluation and Evolution
Hormoz Shahrzad, Risto Miikkulainen
This paper introduces an innovative approach to boost the efficiency and scalability of Evolutionary Rule-based machine Learning (ERL), a key technique in explainable AI. While tra…
Asynchronous Evolution of Deep Neural Network Architectures
Jason Liang, Hormoz Shahrzad, Risto Miikkulainen
Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients)…
Accelerating Evolution Through Gene Masking and Distributed Search
Hormoz Shahrzad, Risto Miikkulainen
In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in o…
EVOTER: Evolution of Transparent Explainable Rule-sets
Hormoz Shahrzad, Babak Hodjat, Risto Miikkulainen
Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly…
DIAS: A Domain-Independent Alife-Based Problem-Solving System
Babak Hodjat, Hormoz Shahrzad, Risto Miikkulainen
A domain-independent problem-solving system based on principles of Artificial Life is introduced. In this system, DIAS, the input and output dimensions of the domain are laid out i…
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Olivier Francon, Santiago Gonzalez, Babak Hodjat +4
There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes…
Regularized Evolutionary Population-Based Training
Jason Liang, Santiago Gonzalez, Hormoz Shahrzad +1
Metalearning of deep neural network (DNN) architectures and hyperparameters has become an increasingly important area of research. At the same time, network regularization has been…
Enhanced Optimization with Composite Objectives and Novelty Pulsation
Hormoz Shahrzad, Babak Hodjat, Camille Dollé +5
An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful…
Enhanced Optimization with Composite Objectives and Novelty Selection
Hormoz Shahrzad, Daniel Fink, Risto Miikkulainen
An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful…
Evolving Deep Neural Networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson +8
The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult t…