Search papers, labs, and topics across Lattice.
100 papers published across 4 labs.
The first-ever line-level dataset of historical Arabic manuscripts with detailed margin annotations unlocks new avenues for understanding non-linear reading orders in ancient texts.
MnemoDyn outperforms state-of-the-art transformer models in reconstructing resting-state fMRI data, revealing new insights into brain dynamics.
Static sample selection in reinforcement fine-tuning is a recipe for suboptimal updates—DIEM adapts dynamically, leading to superior performance on reasoning tasks.
Generating agentic data isn't just about quantity; it's about crafting valid, informative experiences that evolve alongside agents and their environments.
A Hybrid ensemble of XGBoost and TabNet yields a striking annualized return of 51.26%, highlighting the critical role of regime-robust hyperparameter tuning in algorithmic trading.
Static sample selection in reinforcement fine-tuning is a recipe for suboptimal updates—DIEM adapts dynamically, leading to superior performance on reasoning tasks.
Generating agentic data isn't just about quantity; it's about crafting valid, informative experiences that evolve alongside agents and their environments.
A Hybrid ensemble of XGBoost and TabNet yields a striking annualized return of 51.26%, highlighting the critical role of regime-robust hyperparameter tuning in algorithmic trading.
A new dataset reveals the intricate connections between Welsh place names and their environmental contexts, paving the way for innovative research in toponymy and geography.
Recursive Transformers can outperform standard models on limited data, revealing a new pathway to effective scaling in resource-constrained scenarios.
Larger datasets can reveal hidden teacher traits in student models, even from off-task data, potentially reshaping how we approach model distillation.
Training on just 10% of carefully curated trajectories can lead to performance improvements of over 24% in software issue resolution tasks.
SimCast-S2S outperforms traditional forecasting models by effectively combining latent generative modeling with transfer learning, achieving competitive results without extensive atmospheric input data or post-processing.
Four new event-based datasets could redefine the landscape of spiking neural network research by providing the high-quality data needed for robust object classification.
Automated audits using LLMs can uncover hidden biases that traditional methods miss, ensuring fairer candidate-job matching systems.
ColRel reveals that even weak metadata can yield meaningful insights when enhanced with contextual business knowledge, transforming how we navigate complex data lakes.
The categorizer automaton achieves linear state space complexity, revolutionizing how we synthesize policies for maximizing discounted-sum payoffs in Markov decision processes.
Forgetting in language models just got smarter—GRAPHSU slashes knowledge leakage by nearly 50% by targeting support routes, not just forget seeds.
Automated assessment of causal research designs reveals a surprising disconnect between human and machine difficulty, challenging assumptions about expert consensus.
TabuLM outperforms multilingual baselines by over 11 points, showcasing the power of morphology-aware embeddings in low-resource language models.
The first-ever line-level dataset of historical Arabic manuscripts with detailed margin annotations unlocks new avenues for understanding non-linear reading orders in ancient texts.
Skill packages can transform agentic language models, improving their performance by utilizing reusable tool semantics and workflows during pre-training.
Instruction quality is the hidden bottleneck in preference learning, and refining it can dramatically enhance model alignment.
PFGS can reduce word error rates by up to 19.3% compared to random selection, highlighting the critical role of phoneme frequency in TTS augmentation for ASR.
Generating synthetic populations with full reproducibility from public data sources could revolutionize urban studies and demographic modeling.
Local combination synthetic data methods leak substantial privacy, undermining their perceived anonymity in sensitive applications like healthcare.
Only 8.7% of reproducibility-relevant mutations in ML repositories are detected by current validation workflows, revealing a critical oversight in safeguarding research integrity.
Achieving superior accuracy in fetal limb assessment, UniFLM bridges critical gaps in ultrasound image analysis that have long hindered the detection of skeletal dysplasias.
HOLMES achieves a staggering 58.8x speedup in high-dimensional yield estimation while maintaining less than 6% relative error, revolutionizing how we approach failure-center localization.
Aggregating insights from neighboring users' reviews can transform how we tackle data sparsity in recommender systems, leading to better predictions and richer explanations.
Data accessibility is misleading; STREAM reveals how to ensure that what you collect is actually useful for energy performance assessments.
Achieving a new state-of-the-art mIoU of 38.8% in semantic scene completion with a single-sweep, single-sample approach reveals the power of generative diffusion methods in handling extreme class imbalance.
PPE achieves a staggering 83.3% recall in predicting Ebola outbreak zones, outperforming traditional models by over 10 percentage points.
Efficiently estimating high information projections could revolutionize how we approach dimensionality reduction in complex datasets.
Transforming scientific papers into multi-turn generation trajectories not only doubles the training data but also boosts academic writing benchmarks while maintaining reasoning skills.
High-performing cough-based TB classifiers fail to generalize across datasets, revealing that data collection artifacts overshadow disease-related signals.
EXAONE Tabular outperforms tuned ensembles and larger models, achieving top rankings in multiple benchmarks while drastically reducing inference costs.
Unsupervised diffusion pretraining can elevate medical image segmentation performance, achieving up to a 74% improvement in boundary precision without relying on extensive labeled datasets.
Achieving over 98% accuracy with a compact model, CropCop sets a new standard for plant-health recognition while ensuring data integrity through rigorous auditing.
Data citation in large language models is not just a verification issue—it's a complex challenge that demands new frameworks for credit and provenance.
Multi-factor difficulty estimation boosts segmentation performance, achieving state-of-the-art results across multiple architectures.
MoganBert-TR outperforms traditional masked language models by up to 3.7x in Turkish retrieval tasks, redefining benchmarks for Turkish NLP.
Unmatched beliefs in Theory-of-Mind tracking are often valid, and mislabeling them can lead to significant errors in model selection and calibration.
PolyMemDB resolves long-term factual conflicts in AI memory, drastically reducing hallucinations and enhancing user personalization.
Fine-grained dataset distillation can achieve superior performance by focusing on localized evidence rather than just global statistics.
VISA's self-evolving framework not only enhances multimodal instruction synthesis but also adapts in real-time to improve training data quality and model performance.
Active learning can dramatically reduce the annotation burden in summarization tasks, with LOBSTER achieving up to 665x faster query selection without sacrificing performance.
LLMs can reveal critical information from OOXML documents that is invisible in Microsoft Office, with up to 76% of trials exposing hidden facts.
Despite rigorous auditing, the adaptive agent failed to outperform random search, highlighting the challenges in achieving profitability in cryptocurrency trading.
A groundbreaking dataset and framework that significantly enhance ulcer tissue segmentation accuracy, even with limited labeled data.
RefVideo-6M revolutionizes video editing datasets by providing 5 million high-quality editing samples that prioritize visual references over flawed automatic edits.
Achieving zero-shot action localization with minimal annotation, this method effectively estimates unseen actions by innovatively leveraging weakly-supervised pretraining.
SE-CLIP outperforms traditional semi-supervised methods by effectively mining high-confidence samples, transforming how we adapt vision-language models to satellite imagery.
Automating LiDAR annotation can cut down the labor-intensive process of data labeling while achieving near-human accuracy in segmentation tasks.
Incorporating noisy, in-the-wild imagery into CVL models significantly boosts their performance on clean datasets, challenging traditional reliance on high-end sensors.
Unlabelled GNSS data can dramatically enhance PVT accuracy in urban environments, achieving substantial improvements even in unseen harsh conditions.
CloSeR achieves state-of-the-art GCD performance by elegantly decoupling closed-set recognition from open-set discovery, minimizing objective conflicts and enhancing semantic coherence.
Existing detectors fail to reliably distinguish between integrity attacks and sensor faults, with some methods performing at chance level on the most challenging cases.
Standard spreadsheet references can lead to user errors, but Kale's restrictions dramatically reduce these risks, making spreadsheet tasks safer and more reliable.
Only 6.5% of neuro-symbolic AI studies can be reproduced from their published artifacts, exposing a severe reproducibility crisis in the field.
Existing quality assessment models for AI software are failing, highlighting a critical gap that could jeopardize software reliability.
A unified model can outperform localized traffic-behavior models by over 36% in accuracy, revealing the power of data harmonization across intersections.
Human-written album reviews can dramatically enhance music retrieval models, yielding substantial gains in performance for complex queries that typical datasets struggle with.
Benchmark rankings for multilingual embedding models are severely compromised by dataset scarcity, with many relying on a single source, which could mislead evaluations.
Models trained on LAION-BVD achieve state-of-the-art performance in multimodal tasks, showcasing the dataset's potential to redefine video understanding.
Current power outage prediction models may be overhyped, as they often fail to generalize beyond inflated performance metrics derived from flawed evaluation methods.
INCEPT outperforms existing EEG models by prioritizing representation stability over signal reconstruction, achieving top rankings in 26 of 30 evaluation metrics across diverse tasks.
SSL for tabular data can outperform traditional methods, but its effectiveness is highly variable and context-sensitive, particularly in the presence of missing values.
Mechanistic control over data generation reveals hidden model dynamics, leading to more diverse and effective datasets that enhance downstream performance.
MnemoDyn outperforms state-of-the-art transformer models in reconstructing resting-state fMRI data, revealing new insights into brain dynamics.
COCI transforms unstructured Calls for Papers into structured metadata, bridging the gap between informal scholarly communication and formal knowledge systems.
Shifting to structural causal modeling reveals how targeted interventions can be explicitly evaluated, unlocking new insights into student learning dynamics.
Achieving a 57% improvement in image quality for synthetic CMR generation reveals the untapped potential of metadata-aware conditioning in medical imaging.
BALIGN filters out high-risk preference samples, preserving foundational model capabilities while optimizing alignment, achieving the best of both worlds.
Traditional LLMs may excel at semantics, but they miss the mark on capturing the unique workflows of individual culinary creators, revealing a critical gap in procedural generation.
Hard admissibility constraints can shrink candidate spaces by 480x without sacrificing accuracy, revolutionizing how we approach enterprise data mapping.
Digital job language is not just a trend; it reveals deep disparities in how different sectors are adapting to digitalization, with managerial roles leading the charge.
Persian isn't just low-resource; it's annotation-scarce, revealing a complex landscape of NLP resource availability that challenges conventional assumptions.
Models can achieve near-perfect accuracy in resolving conflicting cues, yet their internal mechanisms can differ dramatically, challenging our understanding of model interpretability.
ROBE achieves a remarkable 0.10 increase in F1 score for long-tail event classes, proving that tailored expert classifiers can outperform conventional models in niche historical contexts.
Refined annotations can boost PE segmentation performance more than changes in model training, challenging assumptions about model superiority.
Achieving near state-of-the-art CXR detection with under 10% of the usual annotations could revolutionize medical imaging workflows.
A novel training pipeline that decouples object geometry from semantics and enhances robustness, achieving state-of-the-art performance in cross-city object detection.
Achieving state-of-the-art performance in speech decoding with a dataset that features 80 hours of deep, within-subject MEG data sets a new standard for neural data quality and quantity.
Transforming raw gameplay footage into high-quality training data by effectively removing user interfaces could revolutionize how world models are trained.
Synthetic question generation from knowledge graphs boosts retrieval precision and reasoning performance, even in the absence of labeled data.
Achieving a 75× increase in annotation throughput, RefLAM transforms the scalability of historical Arabic manuscript digitization.
Generating over 203,000 unique web interaction trajectories, BrowserForge significantly boosts model performance on real-world tasks by leveraging the vastness of the open web.
A federated model could revolutionize how medical centers share and enhance device knowledge, ensuring local control while fostering collaborative improvement.
Inconsistencies in cybersecurity research are often driven by flawed evaluation designs rather than the technologies being tested.
The overwhelming majority of ICS cybersecurity datasets ignore critical early-stage threats, limiting the effectiveness of intrusion detection research.
HRV Studio achieves near-perfect agreement with leading HRV analysis tools, revolutionizing reproducibility in cardiovascular research.
UPT can either refine model capabilities or amplify errors, depending on the internal signals used during adaptation.
SPECMINE reveals the intricate relationship between AI-generated specifications and code implementation, providing a treasure trove of data for understanding Spec-Driven Development.
Off-the-shelf VLMs can replace fragile heuristic rules for bitemporal change simulation, unlocking context-aware geospatial reasoning to yield higher-utility synthetic data at a fraction of the sample scale.
Automating the transformation of cultural heritage records into a fully resolvable knowledge graph boosts metadata connectivity by 86%, unlocking previously inaccessible data.
Mapping unlabeled data into a predictive probability space with entropy weighting can dramatically improve active learning efficiency, surpassing traditional methods.
Adapting ECG classification models at inference time can yield a significant performance boost, even in the presence of noisy data and domain shifts.
Generating high-quality synthetic samples for minority classes can dramatically enhance classifier performance in imbalanced time-series tasks.
Light data augmentation and progressive backbone unfreezing can significantly boost the accuracy of bee detection systems, achieving over 97% precision.
Counterfactual annotations reveal how specific interaction failures can lead to drastically different surgical team outcomes, opening new avenues for performance improvement.
Compact and reliable prediction sets can be achieved in healthcare AI even with limited labeled data, thanks to a novel integration of conformal risk minimization and optimal transport.
Non-English Wikipedia entries can dramatically enrich English biographies, revealing overlooked narratives, especially for women from diverse backgrounds.
TCN-AE outperforms traditional TSFMs in anomaly detection while being more resource-efficient, challenging the assumption that larger models are always better.
Even with an impressive $R^2$ of 0.75, EO-ML methods can mislead policymakers due to inherent uncertainties, underscoring the need for robust uncertainty quantification in poverty mapping.
