New model predicts fine-grained types for high-multiplicity entities.
problem Fine-grained entity typing with high type multiplicity.
method Set-prediction approach to high-multiplicity fine-grained typing.
result Model outperforms baselines on Wikipedia-based corpus.
Proposes a method to predict stock movements using fine-grained events from finance news.
problem Lack of specific semantic information in coarse-grained events for stock movement prediction.
method Built a finance event dictionary, extracted fine-grained events, combined with stock trade data, and used distant supervision for training.
result Method outperforms all baselines and shows good generalizability.
Improves biomedical entity linking with latent type modeling.
problem Lack of fine-grained type information for entity disambiguation.
method Jointly models entity disambiguation and latent type learning without direct supervision.
result Significant performance improvements over state-of-the-art techniques.
Unified neural framework for multi-relational recommender systems.
problem Accurately capturing users' fine-grained preferences from diverse feedback types.
method Multi-Relational Memory Network (MRMN) framework that models fine-grained user-item relations and discriminates between feedback types.
result The proposed MRMN model outperforms state-of-the-art algorithms in various recommender scenarios.
We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multi…
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
problem Coarse SEC item codes mislabel routine and significant events.
method Two-stage system tagging 8-K disclosures against a 119-event taxonomy.
result LLM judge finds precision rises to 96% with quality scores.
Replicated attention model in image classification and fine-grained recognition.
problem Improving attention mechanisms in neural networks.
method Implemented the 'Learn to Pay Attention' model in convolutional neural networks.
result Successfully replicated results in image classification and fine-grained recognition.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
problem Complexity of sporting events requires multiple metrics for accurate analysis.
method Multi-task learning with fine-grain spatial data and wide-and-deep learning.
result Predictions are consistent and can be in distribution form.
Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.
problem Reduce cost of urban flow monitoring while maintaining data accuracy and granularity.
method Develops UrbanFM and UrbanPy models to infer fine-grained urban flows from coarse-grained observations.
result UrbanPy model demonstrates favorable performance for larger-scale inference tasks.
Fine-grained gap-dependent regret bounds for reinforcement learning.
problem Achieving optimal regret bounds for reinforcement learning with suboptimality gaps.
method Developed novel analytical frameworks and refined algorithms for UCB-based and non-UCB-based reinforcement learning.
result Established the first fine-grained gap-dependent regret bounds for both UCB-based and non-UCB-based algorithms.
FIGARO generates symbolic music with fine-grained control.
problem Minimal control over generated music sequences.
method Description-to-sequence task, learning conditional distribution of sequences given high-level descriptions.
result State-of-the-art controllable symbolic music generation.
Gene expression programming predicts compression index of fine-grained soils efficiently.
problem Estimating the compression index of fine-grained soils is costly and time-consuming.
method Developed a gene expression programming model using soil parameters.
result The GEP model predicts the compression index more accurately than conventional methods.
DistPre predicts traffic speeds efficiently for large networks.
problem Fine-grained, accurate speed prediction for large-scale transportation networks.
method Customizes LSTM models on a cluster, sharing trained models between detectors.
result Efficient and accurate fine-grained traffic-speed prediction.
Graph-RISE learns image embeddings for ultra-fine-grained semantics.
problem Learning image representations for fine-grained semantics.
method Graph-regularized neural graph learning framework.
result Graph-RISE outperforms state-of-the-art on image classification and triplet ranking.
Fine-grained pretraining improves neural network's ability to learn rare features.
problem Improving generalization in deep learning models.
method Introducing a hierarchical multi-view structure to confine input data distribution.
result Fine-grained pretraining leads to better accuracy on hard downstream test samples.
ECN framework improves training on noisy structured labels.
problem Structured errors in fine-grained annotations lead to biased models.
method Error-Correcting Networks (ECN) framework.
result ECN improves fine-grained annotation prediction.
Paper proposes a new model for fine-grained review classification using weak supervision.
problem Fine-grained analysis of reviews is challenging due to segment-level labels.
method Employed Multiple Instance Learning (MIL) with sigmoid attention mechanism for weak supervision.
result Proposed model outperforms state-of-the-art models in segment-level sentiment classification.
BERT improves fine-grained sentiment classification.
problem Fine-grained sentiment classification of text.
method Used BERT for fine-grained sentiment classification.
result BERT outperforms other models for fine-grained sentiment classification.
Proposes a new method to generate unrestricted adversarial examples.
problem Generating unrestricted adversarial examples without norm constraints.
method Leveraging state-of-the-art generative models to manipulate image fine-grained aspects.
result Our adversarial images look indistinguishable from natural images and can bypass certified defenses.
Efficient algorithms learn from coarse labels instead of fine grained ones.
problem Learning from coarse labels when fine labels are unavailable.
method Formalized coarse label settings, used a reduction for SQs, and provided efficient algorithms.
result Any problem learnable from fine labels can be learned efficiently from coarse labels.
PCONV combines fine-grained and coarse-grained pruning for efficient DNN inference on mobile devices.
problem Achieving high sparsity and accuracy in DNN weight pruning for real-time mobile execution.
method PCONV introduces a new sparsity dimension by combining fine-grained pruning patterns inside coarse-grained structures.
result PCONV outperforms state-of-the-art frameworks in speed and efficiency without accuracy loss.
Proposes a method for weakly-supervised object localization to improve few-shot learning.
problem Challenges of few-shot learning, especially with fine-grained categories.
method Introduces a Self-Attention Based Complementary Module (SAC Module) for weakly-supervised object localization.
result Significantly outperforms state-of-the-art methods on benchmark datasets, especially for fine-grained few-shot tasks.
Online gradient descent can simulate complex computations.
problem Understanding the fine-grained behavior of online gradient descent is hard.
method Proving online gradient descent can encode arbitrary polynomial-space computations.
result It is impossible to reason efficiently about the fine-grained behavior of online gradient descent under weak complexity-theoretic assumptions.
New framework classifies generated images into finer categories.
problem Difficulty in detecting new types of generated images.
method Scalable deep metric learning for multi-class classification.
result Improved detection performance on new types of generated data.
L*ReLU improves deep learning for fine-grained image classification.
problem Fine-grained image classification requires specific AFs.
method Proposes L*ReLU, piece-wise linear AFs for deep learning.
result L*ReLU achieves superior results on FGVC datasets.
New method uses LLMs to generate detailed scientific hypotheses.
problem Generating detailed, actionable scientific hypotheses from coarse initial directions.
method Hierarchical search method that incrementally adds details to hypotheses.
result Hierarchical search method consistently outperforms strong baselines on expert-annotated hypotheses.
ImageNet models transfer well across datasets but not fine-grained ones.
problem Evaluation of transfer learning between ImageNet and other vision tasks.
method Comparison of 16 classification networks on 12 datasets, focusing on ImageNet and fine-tuned settings.
result Strong correlation between ImageNet accuracy and transfer accuracy, but limited transfer to fine-grained tasks.
Improved investment performance with fine-grained LLM tasks.
problem Abstract financial trading systems often overlook real-world workflow intricacies, leading to degraded performance.
method Proposes a multi-agent LLM trading framework that decomposes investment analysis into fine-grained tasks.
result Fine-grained task decomposition significantly improves risk-adjusted returns compared to coarse-grained designs.
Model refines coarse spatial data using diverse auxiliary data sets.
problem Tackles the challenge of refining coarse spatial data with varying auxiliary data granularities.
method Proposes a probabilistic model using Gaussian processes to hierarchically incorporate auxiliary data sets of various granularities.
result Can effectively refine coarse-grained spatial data using auxiliary data sets of different granularities.
Solves dual imbalance in detecting sparse anomalies in MIL.
problem Detecting scarce and sparse anomalous samples in MIL.
method Reformulates MIL as a fine-grained PU learning problem, addressing imbalance at both macro and micro levels.
result Demonstrates effectiveness of BFGPU framework on synthetic and real-world datasets.
System identifies related passages for easier text comprehension.
problem Expensive and limited cross-reference resources for complex texts.
method Fine-grained topic modeling to find topically related verse pairs.
result System can produce cost-effective cross-references.
CLAREL improves zero-shot learning by using per-image semantic supervision and metric rescaling.
problem Fine-grained cross-modal representation learning for zero-shot classification.
method Instance-based deep metric learning in joint visual and textual space, using per-image semantic supervision and metric rescaling.
result CLAREL consistently outperforms existing approaches on fine-grained zero-shot learning datasets.
Unified framework for cost-sensitive ensemble learning.
problem Different misclassification costs in data.
method A unifying framework for cost-sensitive ensemble methods.
result Unified understanding and natural extensions of existing methods.
Paper tackles cross-granularity few-shot learning with meta-embedder.
problem Few-shot learning with coarse labels and fine-grained testing.
method Meta-embedder that optimizes visual and semantic discrimination across coarse and fine classes.
result Meta-embedder achieves effective cross-granularity few-shot classification.
Paper predicts user interests from browsing history and event sequences.
problem Capturing subtle user interests and inter-personal influence.
method Deep prediction method based on two RNNs modeling temporal point process and attention mechanism.
result Model outperforms state-of-the-art methods in fine-grained user interest prediction.
Pipelined Backpropagation trains large models without batches efficiently.
problem Training large models efficiently on hardware with limited batch sizes.
method Fine-grained Pipelined Backpropagation with Spike Compensation and Linear Weight Prediction.
result Fine-grained Pipelined Backpropagation with a batch size of one matches the accuracy of SGD for multiple networks.
We analyze computational limits of modern Hopfield models based on pattern norms.
problem Understanding the efficiency of modern Hopfield models from a fine-grained complexity perspective.
method Fine-grained complexity analysis and upper bound criterion for pattern norms.
result Below a specific norm threshold, efficient variants of modern Hopfield models exist.
New framework learns labels at both bag and graph levels.
problem Learning multi-label classifiers from multi-graph bags.
method Designing scoring functions and rank-loss objective for graph and bag levels; developing sub-gradient descent algorithm.
result Superior performance over state-of-the-art algorithms.
NeuroDiff improves neural network equivalence verification with fine-grained approximations.
problem Verifying the equivalence of compressed neural networks.
method Symbolic and fine-grained approximation technique for differential verification.
result NeuroDiff achieves up to 1000X speedup and 5X accuracy improvement.
Generative profiling improves real-time task timing for varied resource contexts.
problem Inaccurate task timing analysis for complex hardware architectures.
method Nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation for synthesizing context-dependent timing profiles.
result Maximum likelihood accurate execution profiles for unseen resource contexts.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Generative framework learns effective, lower-dimensional models from high-dimensional data.
problem Predicting long-term behavior of complex, multiscale systems with limited data.
method Physics-aware probabilistic model order reduction with latent variables.
result Guaranteed long-term stability and predictive accuracy in multiscale physical systems.
Improves naturalness in TTS samples using quantized VAE and auto-regressive prosody.
problem Discontinuous and unnatural speech from standard VAE priors.
method Discretized latent features using vector quantization (VQ), and separately trained autoregressive (AR) prior model.
result Significantly improves naturalness in random sample generation.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
Tensor network architecture for classification and regression using wavelet transformations.
problem Efficiently performing classification and regression tasks on complex data.
method Tensor network layers based on MERA and MPS, with adaptive fine-graining.
result Adaptive fine-graining improves model performance without loss in accuracy.
Paper proposes redundancy-free features for zero-shot object recognition.
problem Redundant visual features degrade zero-shot object recognition.
method Project original features into a new, statistically independent space.
result RFF-GZSL achieves competitive results on benchmark datasets.
Hierarchical video model learns actions at coarse and fine levels.
problem Understanding complex video actions at different levels of detail.
method A hierarchical architecture that learns coarse-grained to fine-grained actions.
result Models that learn at multiple levels of granularity perform best.
SPL-ADVisE improves deep learning convergence and accuracy.
problem Training deep neural networks with self-paced learning and adaptive embeddings.
method Integrates self-paced learning and deep metric learning using Magnet Loss for dynamic mini-batch selection.
result SPL-ADVisE converges faster and achieves higher accuracy on fine-grained datasets.