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Found 11 papers, 2 papers with code
An Architectural Approach to Enhance Deep Long-Tailed Learning
This ensures that the architecture obtained through our search process incorporates superior components.
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare
Reinforcement Learning (RL) applied in healthcare can lead to unsafe medical decisions and treatment, such as excessive dosages or abrupt changes, often due to agents overlooking common-sense constraints.
Adaptive Hopping for Bluetooth Backscatter using Commodity Edges
To address these issues, this paper presents ChannelDance, a BLE backscatter system that utilizes a low-latency edge server to achieve fast and accurate hopping.
ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers
Notably, ESP-PCT achieves a remarkable accuracy of 93. 2% while reducing the computational requirements (FLOPs) by 76. 9% and memory usage by 78. 2% compared to the existing Point Transformer model simultaneously.
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
In essence, SFPrompt judiciously combines split learning with federated learning to handle these challenges.
Distribution Learning and Its Application in Deep Learning
Departing from the traditional statistical learning framework, PD learning focuses on learning the underlying probability distribution, which is modeled as a random variable within the probability simplex.
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs.
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition
It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better.
Homoscatter: Towards efficient connectivity for ZigBee backscatter system
To address the problems mentioned above, we introduce Homoscatter, a novel ZigBee backscatter system that adopts specific ZigBee devices to generate a single tone and leverages continuous phase change to modulate information, which eliminates spectral leakage.
Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications
The seven basic facial expression classifications are a basic way to express complex human emotions and are an important part of artificial intelligence research.
Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion
For that the survey also highlights remarkable models like tensor factorization model, conditional random field model among others to cater the need to acknowledge aesthetics as an important factor in Apparel recommendation. These AI inspired deep models can pinpoint exactly which certain style resonates best with their customers and they can have an understanding of how the new designs will set in with the community.
