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Found 5 papers, 1 papers with code
WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright Protection
Embedding as a Service (EaaS) has become a widely adopted solution, which offers feature extraction capabilities for addressing various downstream tasks in Natural Language Processing (NLP).
FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10
In this paper, we firstly propose the FeCaffe, i. e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe to enable FPGA to support mainline deep learning development features, e. g. training and inference with Caffe.
Explicit Connection Distillation
In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.
Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems
This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems.
Learning based signal detection for MIMO systems with unknown noise statistics
Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.
