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Found 17 papers, 4 papers with code
Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering
Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers.
Is it an i or an l: Test-time Adaptation of Text Line Recognition Models
While text line recognition models are generally trained on large corpora of real and synthetic data, such models can still make frequent mistakes if the handwriting is inscrutable or the image acquisition process adds corruptions, such as noise, blur, compression, etc.
Weakly supervised information extraction from inscrutable handwritten document images
In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data.
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts
We propose a novel approach that uses a generative adversarial network (GAN) to minimize the Jensen-Shannon divergence between the state-trajectory distributions of the demonstrator and the imitator.
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object.
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
We then study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements.
Novel Class Discovery without Forgetting
Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories.
All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)
Compared to standard multilabel baselines, CHAMP provides improved AUPRC in both robustness (8. 87% mean percentage improvement ) and less data regimes.
Spacing Loss for Discovering Novel Categories
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes.
Test-time Adaptation with Slot-Centric Models
In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.
SITA: Single Image Test-time Adaptation
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source.
Unsupervised Adaptation of Semantic Segmentation Models without Source Data
Unsupervised domain adaptation aims to adapt a model learned on the labeled source data, to a new unlabeled target dataset.
Learning to Select Exogenous Events for Marked Temporal Point Process
Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a wide variety of applications which are characterized using discreteevents localized in continuous time.
Node-Level Differentially Private Graph Neural Networks
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
On Learning to Rank Long Sequences with Contextual Bandits
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses.
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.
The Beauty of Capturing Faces: Rating the Quality of Digital Portraits
Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment.
