Search Results for author:
Found 27 papers, 6 papers with code
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse.
Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In during Sparse Matrix Decomposition
A common problem faced during this decomposition is that even though the given matrix may be very sparse, the decomposition may lead to a denser triangular factors due to fill-in.
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
Generative AI has seen remarkable growth over the past few years, with diffusion models being state-of-the-art for image generation.
Reinforcement Learning Based Sensor Optimization for Bio-markers
Radio frequency (RF) biosensors, in particular those based on inter-digitated capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote sensing, and wireless communication.
Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers
For the subset of highly conductive samples, we employed a second ML model (regression model), to predict their conductivities, yielding an impressive test R2 value of 0. 984.
Structured Low-Rank Tensor Learning
We consider the problem of learning low-rank tensors from partial observations with structural constraints, and propose a novel factorization of such tensors, which leads to a simpler optimization problem.
Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion
Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data.
Light-weight Deep Extreme Multilabel Classification
Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels.
Adaptive Consensus Optimization Method for GANs
The proposed method is fastest to obtain similar accuracy when compared to prominent second order methods.
Angle based dynamic learning rate for gradient descent
In our work, we propose a novel yet simple approach to obtain an adaptive learning rate for gradient-based descent methods on classification tasks.
marl-jax: Multi-Agent Reinforcement Leaning Framework
We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Qualitative Data Augmentation for Performance Prediction in VLSI circuits
Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization.
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs
In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph.
Review of Extreme Multilabel Classification
Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification.
Effects of Spectral Normalization in Multi-agent Reinforcement Learning
A reliable critic is central to on-policy actor-critic learning.
Riemannian Hamiltonian methods for min-max optimization on manifolds
In this paper, we study min-max optimization problems on Riemannian manifolds.
SCIMAT: Science and Mathematics Dataset
In this work, we announce a comprehensive well curated and opensource dataset with millions of samples for pre-college and college level problems in mathematicsand science.
On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models.
Multiphase modelling of glioma pseudopalisading under acidosis
We propose a multiphase modeling approach to describe glioma pseudopalisade patterning under the influence of acidosis.
Direct Opto-Electronic Imaging of 2D Semiconductor - 3D Metal Buried Interfaces
To be specific, potential, conductance and photoluminescence at the buried metal/MoS2 interface are correlated as a function of a variety of metal deposition conditions as well as the type of metal contacts.
Mesoscale and Nanoscale Physics Materials Science Applied Physics Optics
The polar precursor method for solar cycle prediction: comparison of predictors and their temporal range
The polar precursor method is widely considered to be the most robust physically motivated method to predict the amplitude of an upcoming solar cycle. It uses indicators of the magnetic field concentrated near the poles around sunspot minimum.
Solar and Stellar Astrophysics Space Physics
A Survey on Semantic Parsing from the perspective of Compositionality
In section 3 we will consider systems that uses formal languages e. g. $\lambda$-calculus (Steedman, 1996), $\lambda$-DCS (Liang, 2013).
A flux-limited model for glioma patterning with hypoxia-induced angiogenesis
We propose a model for glioma patterns in a microlocal tumor environment under the influence of acidity, angiogenesis, and tissue anisotropy.
Multiscale modeling of glioma pseudopalisades: contributions from the tumor microenvironment
Moreover, we study two different types of scaling and compare the behavior of the obtained macroscopic PDEs by way of simulations.
Deep Attentive Ranking Networks for Learning to Order Sentences
We apply our framework on two tasks: Sentence Ordering and Order Discrimination.
Optimizing Non-decomposable Measures with Deep Networks
We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.
