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Found 19 papers, 15 papers with code
Lovasz Convolutional Networks
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Word embeddings have been widely adopted across several NLP applications.
Observations of Fast Radio Bursts at Frequencies down to 400 Megahertz
Emission in multiple events is seen down to 400 MHz, the lowest radio frequency to which we are sensitive.
High Energy Astrophysical Phenomena
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function.
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning
In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.
Exclusive Supermask Subnetwork Training for Continual Learning
Although there is no forgetting, the performance of SupSup is sub-optimal because fixed weights restrict its representational power.
Self-Chained Image-Language Model for Video Localization and Question Answering
SeViLA framework consists of two modules: Localizer and Answerer, where both are parameter-efficiently fine-tuned from BLIP-2.
Ranked #3 on
Zero-Shot Video Question Answer
on IntentQA
(using extra training data)
TIES-Merging: Resolving Interference When Merging Models
To address this, we propose our method, TRIM, ELECT SIGN & MERGE (TIES-Merging), which introduces three novel steps when merging models: (1) resetting parameters that only changed a small amount during fine-tuning, (2) resolving sign conflicts, and (3) merging only the parameters that are in alignment with the final agreed-upon sign.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to duplication of the network layers into multiple copies as experts; and (b) Redundancy in Experts, as common learning-based routing policies suffer from representational collapse.
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning
There are two dominant approaches: (1) geometry-based data selection for maximizing data diversity in the coreset, and (2) functions that assign difficulty scores to samples based on training dynamics.
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Despite the efficiency of PEFT methods, the size of expert models can make it onerous to retrieve expert models per query over high-latency networks like the Internet or serve multiple experts on a single GPU.
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
