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Found 26 papers, 15 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
We introduce UI-Vision, the first comprehensive, license-permissive benchmark for offline, fine-grained evaluation of computer use agents in real-world desktop environments.
Societal Alignment Frameworks Can Improve LLM Alignment
Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment.
The BrowserGym Ecosystem for Web Agent Research
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs).
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
We use an efficient data curation process to ensure our data is high-quality and license-permissive.
Context is Key: A Benchmark for Forecasting with Essential Textual Information
To address this, we introduce "Context is Key" (CiK), a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities.
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
We also compare the performance of open- and closed-source LLMs and various evaluation strategies.
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents.
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
We run a large-scale benchmark comprising several state-of-the-art LLMs to uncover differences in performance across models of various types and sizes in a context-conditional language modeling setting.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #35 on
Code Generation
on MBPP
Capture the Flag: Uncovering Data Insights with Large Language Models
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations.
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i. e., functions that are minimal in expectation for the ground-truth distribution.
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
Meta-learning framework with applications to zero-shot time-series forecasting
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets?
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
We focus on solving the univariate times series point forecasting problem using deep learning.
Time Series
Time-Series Few-Shot Learning with Heterogeneous Channels
+1
Learning to Learn with Conditional Class Dependencies
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance.
Adversarially Learned Mixture Model
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
HeMIS: Hetero-Modal Image Segmentation
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities.
Ranked #111 on
Semantic Segmentation
on NYU Depth v2
Effective Bayesian Modeling of Groups of Related Count Time Series
Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate.
