Search Results for author:
Found 16 papers, 8 papers with code
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
Next, we leverage a generative foundation model to dynamically expand this compressed set in real-time, enhancing the resolution of these patches and introducing controlled variability to the coreset.
Generative Kaleidoscopic Networks
In other words, the neural networks learn a many-to-one mapping and this effect is more prominent as we increase the number of layers or the depth of the neural network.
Federated Learning with Neural Graphical Models
We develop a FL framework which maintains a global NGM model that learns the averaged information from the local NGM models while keeping the training data within the client's environment.
Knowledge Propagation over Conditional Independence Graphs
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weights show the partial correlation strength between the features.
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
In this paper, we introduce DiversiGATE, a unified framework that consolidates diverse methodologies for LLM verification.
Are uGLAD? Time will tell!
Segmentation of multivariate time series data is a technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system's behavior.
MathPrompter: Mathematical Reasoning using Large Language Models
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.
Neural Graph Revealers
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries.
Methods for Recovering Conditional Independence Graphs: A Survey
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships.
Neural Graphical Models
Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph operations.
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting.
Echo State Speech Recognition
We propose automatic speech recognition (ASR) models inspired by echo state network (ESN), in which a subset of recurrent neural networks (RNN) layers in the models are randomly initialized and untrained.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Learning What Not to Model: Gaussian Process Regression with Negative Constraints
But, what if for an input point '$\bar{\mathbf{x}}$', we want to constrain the GP to avoid a target regression value '$\bar{y}(\bar{\mathbf{x}})$' (a negative datapair)?
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference
Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements.
GLAD: Learning Sparse Graph Recovery
Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure.
