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Found 7 papers, 1 papers with code
Push Quantization-Aware Training Toward Full Precision Performances via Consistency Regularization
Existing Quantization-Aware Training (QAT) methods intensively depend on the complete labeled dataset or knowledge distillation to guarantee the performances toward Full Precision (FP) accuracies.
Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems
In our KERL framework, entity textual descriptions are encoded via a pre-trained language model, while a knowledge graph helps reinforce the representation of these entities.
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks
Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case.
Revisiting Graph based Social Recommendation: A Distillation Enhanced Social Graph Network
We train a model that integrates information from the user-item interaction graph and the user-user social graph and train two auxiliary models that only use one of the above graphs respectively.
Nanoparticle seeded glancing-angle deposition of tip-handle heterostructures for manipulation of individual nanoparticles
The controllable handling of an arbitrary single particle of matter with sub-100 nanometer (nm) dimensions is an essential but unsolved scientific challenge.
Materials Science Mesoscale and Nanoscale Physics
TRec: Sequential Recommender Based On Latent Item Trend Information
Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
Modeling Musical Taste Evolution with Recurrent Neural Networks
Finding the music of the moment can often be a challenging problem, even for well-versed music listeners.
