Novel method diagnoses large language models' reasoning abilities.
problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.
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.
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.
Paper proposes a new model for speech synthesis with better interpretability.
problem Improving interpretability in speech synthesis models.
method Hierarchical, fine-grained latent variable model with conditional variational auto-encoder (VAE).
result The new model allows better interpretability without degrading performance.
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…
Efficiently evaluate generative models at the prompt level using tensor factorization.
problem Fine-grained evaluations of generative models are costly and often misaligned with human judgment.
method Tensor factorization model that merges cheap autorater data with a small set of human gold-standard labels.
result The method provides accurate and tight confidence intervals for model performance.
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is c…
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.
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.
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.
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.
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to tr…
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.
PCNN prunes CNN weights efficiently for hardware acceleration.
problem Efficiently compressing CNN models for hardware acceleration.
method PCNN uses a novel Sparsity Pattern Mask (SPM) to encode and prune weights.
result PCNN achieves up to 8.4X compression with minimal accuracy loss.
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.
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key novelty of this paper is that it shows that using per-image semantic supervision leads to substantial improvement in zero-shot performance ov…
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.
As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data source…
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.
We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces t…
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.
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.
Improved object detection for scientific document images.
problem Current object detectors fail to accurately localize regions in scientific document images.
method Revised R-CNN model with region embedding for fine-grained proposals.
result 17% mAP improvement over standard object detection models.
ADSAGE detects anomalies in graph edge sequences for insider threat detection.
problem Detecting insider threats in fine-grained audit logs using graph and text features.
method Anomaly detection at edge level, supporting numeric, categorical, and text attributes.
result ADSAGE detects anomalies in authentications and email communications effectively.
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.
Heterogeneous GNN improves species distribution modeling.
problem Predicting species occurrences and habitat suitability using environmental factors.
method Graph Neural Networks (GNN) for presence-only species distribution modeling.
result Heterogeneous GNN model outperforms single-species SDMs and baseline models.
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.
User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised…
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.
New method provides fine-grained feedback on interactive student programs.
problem Time-consuming manual grading of interactive student programs.
method Meta-exploration approach using reinforcement learning.
result 94.3% accuracy in providing fine-grained feedback.
This paper evaluates targeted data poisoning attacks by focusing on the hardest samples, improving evaluation and defense strategies.
problem The effectiveness of targeted data poisoning attacks is often overestimated due to average evaluation methods.
method The paper introduces metrics to identify the hardest and easiest to poison samples based on clean model information.
result The proposed metrics reliably stratify samples by poisoning vulnerability, enabling rigorous worst-case evaluation and proactive defense.
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.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
problem Lack of fine-grained company labels for analytics.
method Word2Vec and dimensionality reduction on company website data.
result Semantic company embeddings for various applications.
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.
Generates realistic faces from detailed textual descriptions.
problem Face generation from fine-grained textual descriptions.
method Conditional GAN model with DC-GAN and GAN-CLS loss, using CelebA dataset with generated captions.
result Promising results in generating diverse face images from fine-grained textual descriptions.
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hy…
LDA improves image classification accuracy with fewer features.
problem Fine-grained image classification with pretrained features.
method Supervised dimensionality reduction with LDA before linear probing.
result LDA improves accuracy over full features in 11 out of 12 configurations.
This research improves calorimeter simulations by creating a faster model.
problem Efficiently simulating detailed calorimeter data for high-energy physics.
method Developed a conditional normalizing flow model for superresolution.
result The model successfully reproduces reference distributions.
VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.
problem Estimating value function of a target policy from offline data collected by a behavior policy.
method Proposes VA-OPE, an algorithm that reweights Bellman residual using estimated variance of the value function.
result Achieves a tighter error bound than the best-known result.
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data set…
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.
This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the…
Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention …
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.
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.