Search papers, labs, and topics across Lattice.
40 papers published across 2 labs.
Purely synthetic training data outperforms native real-world supervision on satellite stereo benchmarks, showing that pristine geometric labels and zero temporal noise beat noisy real-world data collection.
Synthetic-to-real pretraining bridges the chronic scarcity of longitudinal clinical imaging, enabling transformers to reliably forecast up to 15 years of spinal disease progression.
Purely synthetic training data outperforms native real-world supervision on satellite stereo benchmarks, showing that pristine geometric labels and zero temporal noise beat noisy real-world data collection.
PhyVisGen, a physically and visually high-fidelity framework for scalable robotic manipulation data generation, introduces an arm-gripper coupling method based on the Incremental Potential Contact (IPC), enabling high-fidelity soft contact throughout complete manipulation trajectories.
A conceptual framework for participatory speech data curation by, for, and with marginalized communities drawing on insights from co-design and knowledge sharing is developed, comprising overlapping and two-way processes of defining a community, project formulation, modes of participation, and personal autonomy.
This paper introduces time-varying exponentiated convolution methods that transform both Gaussian noise and impulse responses into reverberation and modified spectral-decay fields respectively and derives two recursive and fast convolution algorithms that extend into the spherical harmonic domain.
This work proposes UniK, a common platform for both digital and physical AI classes, covering the full knowledge lifecycle across modalities from rich text and video to molecular data and sensor telemetry, built on Polymath Retrieval with no task-specific fine-tuning.
EdgeGen is a synthetic task generation framework that extracts compliance rules from an agent's specification and uses them to generate database-grounded edge-case tasks designed to violate these rules, and when combined with existing synthetic data generation techniques, enables agent improvement through finetuning and harness optimization.
Protein annotation doesn't have to be trapped in rigid ontologies: a 4-bit 3B LLM can generate curator-grade functional descriptions directly from raw amino acid sequences.
A physics-enhanced machine learning approach for the system identification of Linear Time-Varying systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Kalman Filter is proposed.
This work generates a robust synthetic dataset of forward DTOF using exact Monte Carlo simulations at multiple source-detector distances and proposes a machine learning framework as an alternative approach to reconstruct the optical properties of a bilayered medium, benchmarking its efficiency and accuracy against model-based algorithms.
Three compact scratch-trained computer-vision models are benchmarked on the Notre Dame subset of LivDet-Iris 2017 under PAI-driven domain shift and environmental degradation to demonstrate deployment readiness under unknown PAIs.
The results suggest that gradient-based attribution can identify data responsible for subliminal learning in some settings, but that some approximations are more reliable than others.
This research in progress paper investigates different approaches on how to combine JETs with ML-methods in a hybrid manner and presents specialized models to increase the detection performance and validity of anomaly detection results to improve audit efficiency.
This work highlights that diagnosing and factually augmenting data to long-tail deficits is essential for robust scientific retrieval, and presents REPAIR, a self-evolving data augmentation framework for scientific dense retrievers.
For the datasets and models considered, pixel-level image-fidelity metrics alone may not consistently capture the task-relevant realism of synthetic sonar observations and motivate the use of task-aware evaluation for synthetic sensor data intended for robotic perception.
MiST (Mid-trained Security Transformer), a suite of 8B and 32B models that achieve strong performance on public cybersecurity benchmarks are presented and it is shown that MiST provides a stronger initialization for downstream task-specific fine-tuning adaptation and reinforcement learning.
