Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers
Recurrent neural networks are a powerful means in diverse applications. We show that, together with so-called conceptors, they also allow fast learning, in contrast to other deep learning methods. In addition, a relatively small number of examples suffices to train neural networks with high accuracy. We demonstrate this with two applications, namely speech recognition and detecting car driving maneuvers. We improve the state of the art by application-specific preparation techniques: For speech recognition, we use mel frequency cepstral coefficients leading to a compact representation of the frequency spectra, and detecting car driving maneuvers can be done without the commonly used polynomial interpolation, as our evaluation suggests.
Code (0)
등록된 구현이 없습니다.
Tasks
speech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Controlling Recurrent Neural Networks by Diagonal Conceptors
The human brain is capable of learning, memorizing, and regenerating a panoply of temporal patterns. A neuro-dynamical mechanism called conceptors offers a method for controlling the dynamics of a recurrent neural networ…
Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering
Large language models have transformed AI, yet reliably controlling their outputs remains a challenge. This paper explores activation engineering, where outputs of pre-trained LLMs are controlled by manipulating their ac…
In-Context LearningConceptors: an easy introduction
Conceptors provide an elementary neuro-computational mechanism which sheds a fresh and unifying light on a diversity of cognitive phenomena. A number of demanding learning and processing tasks can be solved with unpreced…
DiversityOvercoming Catastrophic Interference by Conceptors
Catastrophic interference has been a major roadblock in the research of continual learning. Here we propose a variant of the back-propagation algorithm, "conceptor-aided back-prop" (CAB), in which gradients are shielded …
Continual LearningFast Labeling and Transcription with the Speechalyzer Toolkit
We describe a software tool named Speechalyzer which is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java a…
Audio ClassificationBenchmarkingClassificationGeneral Classification+6