13 accepted papers
The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of explor…
With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, recent researchers have actively explored the potential of LLMs for recommendation systems by converting the input data into textual sentences through prompt templates. Although semanti…
Personalized federated learning is a new paradigm to address heterogeneous problems (e.g. issues with non-i.i.d. data) in federated learning. However, existing personalized federated learning methods lack standards for how personalized and shared parts of the models are designed. Sometimes, manual d…
Revolutionizing the field of deep learning Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work we propose our definition of…
Large language models (LLMs) have been routinely used to solve various tasks using step-by-step reasoning. However, the structure of intermediate reasoning steps, or *thoughts*, is rigid and unidirectional, such as chains, trees, or acyclic-directed graphs. Consequently, the resulting inflexible and…
Dataset condensation aims to condense the original training dataset into a small synthetic dataset for data-efficient learning. The recently proposed dataset condensation techniques allow the model trainers with limited resources to learn acceptable deep learning models on a small amount of syntheti…
Visual grounding (VG) aims to align the correct regions of an image with a natural language query about that image. We found that existing VG methods are trapped by the single-stage grounding process that performs a sole evaluate-and- rank for meticulously prepared regions. Their performance depends…
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distinct generative model and design an objective function that encourages the generative mode…
Different from its commonly studied scenario to centrally store clients’ data in institutions, which implicitly neglects clients’ data privacy, we study cross-silo federated learning in a preferable setting to keep private data on clients, and train the global model with a three-layer structure, whe…
This paper presents {\em Gem}, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task. Then we train a causal explanation mod…
Graph-based semi-supervised learning has been shown to be one of the most effective classification approaches, as it can exploit connectivity patterns between labeled and unlabeled samples to improve learning performance. However, we show that existing techniques perform poorly when labeled data are…
Training deep neural networks requires an exorbitant amount of computation resources, including a heterogeneous mix of GPU and CPU devices. It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount…
Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these device…