A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper addresses instance-level label prediction in MIL, improving performance compared to existing methods.
ISAHP discovers instance-level causal structures in event sequences.
InstanceFlow visualizes classifier confusion over training epochs.
The paper analyzes how deep neural networks handle noisy labels and finds disparate impacts.
Paper proposes M3Lcmf for better M3L performance.
Improves key instance detection in MIL models by using neural network inversion with sparseness constraint.
Proposes Contrastive Clustering for improved clustering performance.
This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanis…
Paper proposes a method to generate instance labels from weakly supervised data.
Low level features like edges and textures play an important role in accurately localizing instances in neural networks. In this paper, we propose an architecture which improves feature pyramid networks commonly used instance segmentation networks by incorporating low level features in all layers of the pyramid in an o…
Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffe…
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise instance-level interpretability for any complex machine learning model. …
A new SSL method uses instance-dependent thresholds to improve accuracy.
Paper improves deep learning for instance-level classification from label proportions.
New method improves unsupervised feature learning for natural data.
RL agents fail to generalize to unseen environments, even when dynamics are similar.
Paper tackles rDR classification and lesion segmentation using self-supervised equivariant learning and attention-based MIL.
Graph neural networks improve MIL performance without losing interpretability.
End-to-end method learns geometry and appearance for multi-view object detection.
Easyllp simplifies LLP, achieving low task loss at individual instance level.
TaRP predicts missing relations in KGs using type and instance-level info.
This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.
The paper proposes a method to transfer knowledge across different settings using causal theory.
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. Previous studies have also extended the binary-class case into multi-classes. However, many applications, such as object detection and keyframe extraction cannot provide consistent instance-label pairs, while LDA …
The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning experiments to compare learning algorithms. This also requires implementation of another algorithm for comparison, that may not always be correc…
Gradual ML improves ALSA with less manual labeling.
A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The idea underlying the modification is very simple and stems from the confidence screening mechanism idea proposed by Pang et al. to simplify the Deep Forest …
Bayesian algorithms improve crowdsourcing with label and instance constraints.
A method uses confidence scores to handle noisy labels for each instance.
Unified method for local GBDT feature contributions.
Dataset analyzes tweets' impact on stock returns.
Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid …
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situ…
Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each obj…
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…
Optimizes query routing to LLMs under cost and resource constraints.
Solves dual imbalance in detecting sparse anomalies in MIL.
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensional linear subspace, or examine the relevance of a test instance to every label via a linear scan. …
Imbalanced Learning is an important learning algorithm for the classification models, which have enjoyed much popularity on many applications. Typically, imbalanced learning algorithms can be partitioned into two types, i.e., data level approaches and algorithm level approaches. In this paper, the focus is to develop a…
ICE proposes a new loss function for deep metric learning.
We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an ins…
Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual se…
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity -- the portion of positive instances in a bag -- can be explored in weakly supervise…
G-FIGS uses instance weights to create interpretable models from diverse data.
Develops a new feature selection method using deep-learning saliency.
Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy …