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Found 14 papers, 1 papers with code
High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation
This paper proposes a deep video compression method to simultaneously encode multiple frames with Frame-Conv3D and differential modulation.
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach
We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).
Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction
Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to testify if the prediction model satisfies the inherent temporal independence of an interventional distribution.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer.
Recent Advances in Neural Text Generation: A Task-Agnostic Survey
In recent years much effort has been devoted to applying neural models to the task of natural language generation.
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
In this work, we argue that one of the typical formulations of VAEs in multi-agent modeling suffers from an issue we refer to as social posterior collapse, i. e., the model is prone to ignoring historical social context when predicting the future trajectory of an agent.
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning
Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment.
Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation.
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs
To accelerate deep CNN models, this paper proposes a novel spatially adaptive framework that can dynamically generate pixel-wise sparsity according to the input image.
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network
Current methods for long-term trajectory prediction cannot guarantee the physical feasibility of predicted distribution.
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.
