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Found 19 papers, 2 papers with code
KAN based Autoencoders for Factor Models
Inspired by recent advances in Kolmogorov-Arnold Networks (KANs), we introduce a novel approach to latent factor conditional asset pricing models.
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
To gain insights into the neural unit dynamics, we theoretically analyze the model's convergence property via a universal approximation theorem on learning sequential mappings, which is under-explored in the CIL community.
DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving
We utilize the challenging driving scenarios from the CARLA leaderboard 2. 0, which involve high-speed driving and lane-changing, and propose a rule-based expert policy to control the vehicle and generate ground truth labels for its reasoning process across different driving aspects and the final decisions.
LiMAML: Personalization of Deep Recommender Models via Meta Learning
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives.
Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding
Deep neural networks are susceptible to catastrophic forgetting when trained on sequential tasks.
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
IF2Net: Innately Forgetting-Free Networks for Continual Learning
Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge.
Multi-View Class Incremental Learning
Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance.
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving
In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms.
Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
Clutter Edges Detection Algorithms for Structured Clutter Covariance Matrices
This letter deals with the problem of clutter edge detection and localization in training data.
Robust Navigation for Racing Drones based on Imitation Learning and Modularization
This paper presents a vision-based modularized drone racing navigation system that uses a customized convolutional neural network (CNN) for the perception module to produce high-level navigation commands and then leverages a state-of-the-art planner and controller to generate low-level control commands, thus exploiting the advantages of both data-based and model-based approaches.
BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training
The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases.
Inject Rubrics into Short Answer Grading System
Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio.
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
We present a training pipeline for the autonomous driving task given the current camera image and vehicle speed as the input to produce the throttle, brake, and steering control output.
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size.
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems.
