A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations. On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph EmbeddingsQuantizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bit Error Tolerance Metrics for Binarized Neural Networks
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tuned trading accuracy with energy consump…
Approaching the Harm of Gradient Attacks While Only Flipping Labels
Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly availability attacks -adding imperceptible perturbations to training data, ther…
Data PoisoningCorpus Poisoning via Approximate Greedy Gradient Descent
Dense retrievers are widely used in information retrieval and have also been successfully extended to other knowledge intensive areas such as language models, e.g., Retrieval-Augmented Generation (RAG) systems. Unfortuna…
Information RetrievalRAGRetrievalRetrieval-augmented GenerationQFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
A rise in Advanced Persistent Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees. FlipIt is a security game that models …
OpenAI GymQ-Learningreinforcement-learningReinforcement Learning+1A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks
The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural…