Search Results for author:
Found 18 papers, 4 papers with code
Power Failure Cascade Prediction using Graph Neural Networks
We consider the problem of predicting power failure cascades due to branch failures.
Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping
We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms.
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM).
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization
It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure.
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling
Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models.
Evaluating the Robustness to Instructions of Large Language Models
The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks.
A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction
In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner.
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
MergeComp: A Compression Scheduler for Scalable Communication-Efficient Distributed Training
It can even achieve a scaling factor of distributed training up to 99% over high-speed networks.
DAGMapper: Learning to Map by Discovering Lane Topology
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost.
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations.
Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate.
Dendritic-Inspired Processing Enables Bio-Plausible STDP in Compound Binary Synapses
Brain-inspired learning mechanisms, e. g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network.
Enabling Bio-Plausible Multi-level STDP using CMOS Neurons with Dendrites and Bistable RRAMs
Large-scale integration of emerging nanoscale non-volatile memory devices, e. g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems.
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing.
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning
Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system.
$l_1$-regularized Outlier Isolation and Regression
This paper proposed a new regression model called $l_1$-regularized outlier isolation and regression (LOIRE) and a fast algorithm based on block coordinate descent to solve this model.
