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Found 10 papers, 1 papers with code
Learning Equi-angular Representations for Online Continual Learning
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
Just Say the Name: Online Continual Learning with Category Names Only via Data Generation
In real-world scenarios, extensive manual annotation for continual learning is impractical due to prohibitive costs.
Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification
In the field of speaker verification, session or channel variability poses a significant challenge.
Leveraging Skill-to-Skill Supervision for Knowledge Tracing
To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the problems.
Encoder-decoder multimodal speaker change detection
The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications.
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements.
NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks
We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning.
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
The approximate probability of each word can be estimated with only a small part of the weight matrix by using a few large singular values and the corresponding elements for most of the words.
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA.
Online Keyword Spotting with a Character-Level Recurrent Neural Network
In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech.
