Search Results for author:
Found 12 papers, 8 papers with code
Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations
We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation.
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model
In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI).
IHCV: Discovery of Hidden Time-Dependent Control Variables in Non-Linear Dynamical Systems
However, it remains challenging to model hidden non-observable control variables governing switching between different dynamical regimes.
LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions
The LEP-AD model scales favorably in performance with the size of training data.
Ranked #1 on
Protein Language Model
on DAVIS-DTA
Evolving Neural Networks through a Reverse Encoding Tree
Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines.
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research.
Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
Complex data usually results from the interaction of objects produced by different generating mechanisms.
Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification
We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm.
Data Structures and Algorithms Information Theory Information Theory Physics and Society
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space.
Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity
The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results.
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity
We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low.
Information Theory Computational Complexity Information Theory H.1.1
