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Found 10 papers, 3 papers with code
Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners
In-context learning, which offers substantial advantages over fine-tuning, is predominantly observed in decoder-only models, while encoder-decoder (i. e., seq2seq) models excel in methods that rely on weight updates.
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data.
Dense but Efficient VideoQA for Intricate Compositional Reasoning
It is well known that most of the conventional video question answering (VideoQA) datasets consist of easy questions requiring simple reasoning processes.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks.
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation
Although previous approaches pre-define the type of dataset bias to prevent the network from learning it, recognizing the bias type in the real dataset is often prohibitive.
Ranked #3 on
Facial Attribute Classification
on bFFHQ
Learning Debiased Representation via Disentangled Feature Augmentation
To this end, our method learns the disentangled representation of (1) the intrinsic attributes (i. e., those inherently defining a certain class) and (2) bias attributes (i. e., peripheral attributes causing the bias), from a large number of bias-aligned samples, the bias attributes of which have strong correlation with the target variable.
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected.
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop
By demonstrating how our system can collect large amounts of data at little to no cost, we envision similar systems being used to improve performance on other tasks in the future.
