A waste classification method based on a multilayer hybrid convolution neural network
C Shi, C Tan, T Wang, L Wang - Applied Sciences, 2021 - mdpi.com
With the rapid development of deep learning technology, a variety of network models for
classification have been proposed, which is beneficial to the realization of intelligent waste …
classification have been proposed, which is beneficial to the realization of intelligent waste …
A reliable and robust deep learning model for effective recyclable waste classification
In response to the growing waste problem caused by industrialization and modernization,
the need for an automated waste sorting and recycling system for sustainable waste …
the need for an automated waste sorting and recycling system for sustainable waste …
Autofreeze: Automatically freezing model blocks to accelerate fine-tuning
With the rapid adoption of machine learning (ML), a number of domains now use the
approach of fine tuning models which were pre-trained on a large corpus of data. However …
approach of fine tuning models which were pre-trained on a large corpus of data. However …
Language generation via combinatorial constraint satisfaction: A tree search enhanced Monte-Carlo approach
Generating natural language under complex constraints is a principled formulation towards
controllable text generation. We present a framework to allow specification of combinatorial …
controllable text generation. We present a framework to allow specification of combinatorial …
An approximate sampler for energy-based models with divergence diagnostics
Energy-based models (EBMs) allow flexible specifications of probability distributions.
However, sampling from EBMs is non-trivial, usually requiring approximate techniques such …
However, sampling from EBMs is non-trivial, usually requiring approximate techniques such …
Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs
Energy-Based Models (EBMs) allow for extremely flexible specifications of probability
distributions. However, they do not provide a mechanism for obtaining exact samples from …
distributions. However, they do not provide a mechanism for obtaining exact samples from …
[PDF][PDF] Constraint Satisfaction Driven Natural Language Generation: A Tree Search Embedded MCMC Approach
We provide a general framework for the constrained natural language generation. In this
framework, sentences are generated by sampling from a probability distribution that is …
framework, sentences are generated by sampling from a probability distribution that is …