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Found 8 papers, 2 papers with code
DietCNN: Multiplication-free Inference for Quantized CNNs
The rising demand for networked embedded systems with machine intelligence has been a catalyst for sustained attempts by the research community to implement Convolutional Neural Networks (CNN) based inferencing on embedded resource-limited devices.
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity
The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL) agents for controlling the growing portfolio of network services.
Penalizing Proposals using Classifiers for Semi-Supervised Object Detection
Obtaining gold standard annotated data for object detection is often costly, involving human-level effort.
Counterexample Guided RL Policy Refinement Using Bayesian Optimization
The first component is an approach to discover failure trajectories using Bayesian optimization over multiple parameters of uncertainty from a policy learnt in a model-free setting.
Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving
Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have demonstrated impressive performance in complex tasks, including autonomous driving.
Semi-Lexical Languages -- A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision
Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world.
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series
We aim to mine temporal causal sequences that explain observed events (consequents) in time-series traces.
Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures
The proposed algorithms use a best first search technique and report the solutions using an implicit representation ordered by cost.
