6 papers
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
Hedi Zisling, Liran Nochumsohn, Ilan Naiman +2
Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-ser…
FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video
Rotem Ezra, Hedi Zisling, Nimrod Berman +5
Diffusion models have become state-of-the-art generative models for images, audio, and video, yet enabling fine-grained controllable generation, i.e., continuously steering specifi…
Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting
Liran Nochumsohn, Raz Marshanski, Hedi Zisling +1
Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across diverse datasets. Large pre-trained mo…
A Multi-Task Learning Approach to Linear Multivariate Forecasting
Liran Nochumsohn, Hedi Zisling, Omri Azencot
Accurate forecasting of multivariate time series data is important in many engineering and scientific applications. Recent state-of-the-art works ignore the inter-relations between…
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting
Liran Nochumsohn, Michal Moshkovitz, Orly Avner +2
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting t…
Data Augmentation Policy Search for Long-Term Forecasting
Liran Nochumsohn, Omri Azencot
Data augmentation serves as a popular regularization technique to combat overfitting challenges in neural networks. While automatic augmentation has demonstrated success in image c…