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Found 10 papers, 3 papers with code
Training Transformers with 4-bit Integers
To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them.
Masked Motion Encoding for Self-Supervised Video Representation Learning
The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions.
Ranked #2 on
Self-Supervised Action Recognition
on HMDB51
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment.
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation
Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C).
Biofilms as self-shaping growing nematics
Active nematics are the nonequilibrium analog of passive liquid crystals in which anisotropic units consume free energy to drive emergent behavior.
Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling
However, the performance of the proposed CNN model varies: for evaluation tasks that are consistent with the trained network type, the proposed CNN model consistently achieves accurate evaluations of both attack curves and robustness values across all removal scenarios.
Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification
However, as data sizes grow, traditional methods for constructing and retraining decision trees become increasingly slow, scaling polynomially with the number of training examples.
Expressive variational quantum circuits provide inherent privacy in federated learning
However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Privacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes.
