Search Results for author:
Found 15 papers, 2 papers with code
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model.
Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models
Normally, an effective way to reduce the CPD size is to use a binary factorization (BF) algorithm to decompose the statistical or arithmetic functions in the CPD by factorizing the number of connected parent nodes to sets of size two.
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
The Darwin3 chip supports up to 2. 35 million neurons, making it the largest of its kind in neuron scale.
Random resistive memory-based deep extreme point learning machine for unified visual processing
Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
Pruning random resistive memory for optimizing analogue AI
Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network.
New Characterizations and Efficient Local Search for General Integer Linear Programming
We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes.
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Thus, in this paper, we propose FreeEagle, the first data-free backdoor detection method that can effectively detect complex backdoor attacks on deep neural networks, without relying on the access to any clean samples or samples with the trigger.
Automatic Scene-based Topic Channel Construction System for E-Commerce
To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
On the computational solution of vector-density based continuum dislocation dynamics models: a comparison of two plastic distortion and stress update algorithms
The coupling between these two sets of equations is such that dislocation transport gives rise to the evolution of plastic distortion (strain), while the evolution of the latter fixes the stress from which the dislocation velocity field is found via a mobility law.
Materials Science
Incorporating point defect generation due to jog formation into the vector density-based continuum dislocation dynamics approach
The results also show that a higher hardening rate and a higher dislocation density are obtained when the point defect generation mechanism is coupled to dislocation dynamics.
Materials Science
A Hardware Friendly Unsupervised Memristive Neural Network with Weight Sharing Mechanism
Memristive neural networks (MNNs), which use memristors as neurons or synapses, have become a hot research topic recently.
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
Infinite Hidden Semi-Markov Modulated Interaction Point Process
The proposed approach can simultaneously model both the observations and arrival times of temporal events, and determine the number of latent states from data.
Region Based Approximation for High Dimensional Bayesian Network Models
Performing efficient inference on Bayesian Networks (BNs), with large numbers of densely connected variables is challenging.
Performing Bayesian Risk Aggregation using Discrete Approximation Algorithms with Graph Factorization
Risk aggregation is a popular method used to estimate the sum of a collection of financial assets or events, where each asset or event is modelled as a random variable.
