Mark Coates

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. This allows us to fully leverage the high dimensional modeling capability of modern generative models. Our model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.

2023-10-26

ArXiv (prépublication)

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

Florence Regol

Joud Chataoui

2023-10-13

ArXiv (prépublication)

Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification

Muberra Ozmen

Joseph Cotnareanu

Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (voir plus). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70\% in terms of example-based F1 score.

2023-09-24

ArXiv (prépublication)

Neural Graph Generation from Graph Statistics.

Kiarash Zahirnia

Yaochen Hu

Oliver Schulte

Neural Graph Generation from Graph Statistics

Kiarash Zahirnia

Yaochen Hu

Oliver Schulte

2023-09-21

NeurIPS.cc/2023/Conference (poster)

Motion In-Betweening via Deep <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="oreshkin-ieq1-3309107.gif"/></alternatives></inline-formula>-Interpolator

Boris Oreshkin

Antonios Valkanas

Félix Harvey

Louis-Simon Ménard

Florent Bocquelet

We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a dee… (voir plus)p learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline. We empirically demonstrate the strength of our approach on publicly available datasets achieving state-of-the-art performance. We further generalize these results by showing that the

2023-08-28

IEEE Transactions on Visualization and Computer Graphics (publié)

Bidirectional Learning for Offline Model-based Biological Sequence Design

Can Chen

Yingxue Zhang

Xue (Steve) Liu

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Evaluation of Categorical Generative Models - Bridging the Gap Between Real and Synthetic Data

Florence Regol

Anja Kroon

The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicabil… (voir plus)ity. Evaluation on synthetic datasets can be a powerful tool to provide a better understanding of a model’s strengths, weaknesses and overall capabilities. Gaining these insights can be particularly important for generative modeling as the target quantity is completely unknown. Multiple issues related to the evaluation of generative models have been reported in the literature. We argue those problems can be avoided by an evaluation based on ground truth. General criticisms of synthetic experiments are that they are too simplified and not representative of practical scenarios. As such, our experimental setting is tailored to a realistic generative task. We focus on categorical data and introduce an appropriately scalable evaluation method. Our method involves tasking a generative model to learn a distribution in a high-dimensional setting. We then successively bin the large space to obtain smaller probability spaces where meaningful statistical tests can be applied. We consider increasingly large probability spaces, which correspond to increasingly difficult modeling tasks, and compare the generative models based on the highest task difficulty they can reach before being detected as being too far from the ground truth. We validate our evaluation procedure with synthetic experiments on both synthetic generative models and current state-of-the-art categorical generative models.

2023-06-04

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

Haolun Wu

Yingxue Zhang

Chen Ma

Wei Guo

Ruiming Tang

Xue (Steve) Liu

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and i… (voir plus)tem representations. Many state-of-the-art (SOTA) methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks (GNNs) to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information (tags) associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts which is the correct pairing between the representations obtained from the users that have interacted with this item and the tags assigned to it. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the users’ decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose a user intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.

2023-04-03

2023 IEEE 39th International Conference on Data Engineering (ICDE) (publié)

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma

Chen Lin

Derek Lim

Adriana Romero Soriano

Puneet K. Dokania

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (voir plus)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (publié)

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma

Chen Lin

Derek Lim

Adriana Romero Soriano

Puneet K. Dokania

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (voir plus)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) — a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive — it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (publié)

Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network

Mehrtash Mehrabi

Walid Masoudimansour

Yingxue Zhang

Jie Chuai

Zhitang Chen

Jianye Hao

Yanhui. Geng

2023-01-01

AAAI (publié)