Search Results for author:
Found 10 papers, 2 papers with code
Evaluating Large Language Models in Analysing Classroom Dialogue
Recognizing the knowledge-intensive and labor-intensive nature of traditional qualitative methods in educational research, this study investigates the potential of LLM to streamline and enhance the analysis process.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
LayerNAS: Neural Architecture Search in Polynomial Complexity
For a model architecture with $L$ layers, we perform layerwise-search for each layer, selecting from a set of search options $\mathbb{S}$.
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC).
Data-Free Neural Architecture Search via Recursive Label Calibration
We identify that the NAS task requires the synthesized data (we target at image domain here) with enough semantics, diversity, and a minimal domain gap from the natural images.
MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations.
SAFE-DNN: A Deep Neural Network with Spike Assisted Feature Extraction for Noise Robust Inference
We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs.
Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity
In addition, we show that the new algorithm can be used for designing a robust ReRAM based SNN accelerator that has strong resilience to device variation.
ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction
ScieNet integrates unsupervised learning using spiking neural network (SNN) for unsupervised contextual informationextraction with a back-end DNN trained for classification.
HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems
In this paper, we present HybridNet, a framework that integrates data-driven deep learning and model-driven computation to reliably predict spatiotemporal evolution of a dynamical systems even with in-exact knowledge of their parameters.
