Search Results for author:
Found 9 papers, 3 papers with code
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Consequently, there has been a recent surge in utilizing pre-trained large language models (LLMs) with token adaptations for TS forecasting.
AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%.
Multivariate Time Series Forecasting
Representation Learning
+2
TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
The explanations are in terms of the SHAP values obtained by applying the TreeSHAP algorithm on a surrogate model that learns a mapping between the interpretable feature space and the forecast of the black-box model.
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets.
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems
Robust speaker recognition, including in the presence of malicious attacks, is becoming increasingly important and essential, especially due to the proliferation of several smart speakers and personal agents that interact with an individual's voice commands to perform diverse, and even sensitive tasks.
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting
In this paper, we address the task of characterizing acoustic scenes in a workplace setting from audio recordings collected with wearable microphones.
Robust speaker recognition using unsupervised adversarial invariance
In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations.
Neural Predictive Coding using Convolutional Neural Networks towards Unsupervised Learning of Speaker Characteristics
Two sets of experiments are done in different scenarios to evaluate the strength of NPC embeddings and compare with state-of-the-art in-domain supervised methods.
