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Found 33 papers, 18 papers with code
L3Ms -- Lagrange Large Language Models
Supervised fine-tuning (SFT) and alignment of large language models (LLMs) are key steps in providing a good user experience.
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems.
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing representations in this field.
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Automated Few-shot Classification with Instruction-Finetuned Language Models
We observe, in the context of classification tasks, that instruction finetuned language models exhibit remarkable prompt robustness, and we subsequently propose a simple method to eliminate the need for handcrafted prompts, named AuT-Few.
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher training practices and effective knowledge transfer.
XTab: Cross-table Pretraining for Tabular Transformers
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data.
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts.
Towards Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response.
Parameter-Efficient Fine-Tuning Design Spaces
We discover the following design patterns: (i) group layers in a spindle pattern; (ii) allocate the number of trainable parameters to layers uniformly; (iii) tune all the groups; (iv) assign proper tuning strategies to different groups.
Learning Multimodal Data Augmentation in Feature Space
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems.
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning
Pre-trained large language models can efficiently interpolate human-written prompts in a natural way.
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated promising results in long-term time-series forecasting in recent years.
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
Multimodal image-text models have shown remarkable performance in the past few years.
A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data
The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers.
Visual Prompt Tuning for Test-time Domain Adaptation
First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.
Ranked #6 on
Domain Adaptation
on VisDA2017
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
With the explosive growth of the spatiotemporal Earth observation data in the past decade, data-driven models that apply Deep Learning (DL) are demonstrating impressive potential for various Earth system forecasting tasks.
Ranked #1 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Removing Batch Normalization Boosts Adversarial Training
In addition, NoFrost achieves a $23. 56\%$ adversarial robustness against PGD attack, which improves the $13. 57\%$ robustness in BN-based AT.
Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar
We introduce NSEdit (neural-symbolic edit), a novel Transformer-based code repair method.
Ranked #1 on
Code Repair
on CodeXGLUE - Bugs2Fix
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well.
Ranked #2 on
Binary Classification
on kickstarter
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing
We aim to identify how different components in the KD pipeline affect the resulting performance and how much the optimal KD pipeline varies across different datasets/tasks, such as the data augmentation policy, the loss function, and the intermediate representation for transferring the knowledge between teacher and student.
Multimodal AutoML on Structured Tables with Text Fields
We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how?
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Spatiotemporal Modeling for Crowd Counting in Videos
To exploit the otherwise very useful temporal information in video sequences, we propose a variant of a recent deep learning model called convolutional LSTM (ConvLSTM) for crowd counting.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
