OpenRaiser/Intern-Atlas huggingface dataset:
Bayesian assembly of 3D axially symmetric shapes from fragments
Authors: Andrew R. Willis, David B. Cooper
Venue: Conference CVPR 2004
Year: 2004

We present a complete system for the purpose of automatically assembling 3D pots given 3D measurements of their fragments commonly called sherds. A Bayesian approach is formulated which, at present, models the data given a set of sherd geometric parameters. Dense sherd measurement data is obtained by scanning the outside surface of each sherd with a laser scanner. Mathematical models, specified by a set of geometric parameters, represent the sherd outer surface and break curves on the outer surface (where two sherds have broken apart). Optimal alignment of assemblies of sherds, called configurations, is implemented as maximum likelihood estimation (MLE) of the surface and break curve parameters given the measured sherd data for all sherds in a configuration. The assembly process starts with a fast clustering scheme which approximates the MLE solution for all sherd pairs, i.e., configurations of size 2, using a subspace of the geometric parameters, i.e., the sherd break curves. More accurate MLE values based on all parameters, i.e., sherd alignments, are computed when sherd pairs are merged with other sherd configurations. Merges take place in order of constant probability starting at the most probable configuration. This method is robust to missing sherds or groups of sherds which contain sherds from more than one pot. The system represents at least three significant advances over previous 3D puzzle solving approaches : (1) a Bayesian framework which allows for easily combining diverse types of information extracted from each sherd, (2) a search which reduces comparisons on unlikely configurations, and (3) a robust computationally reasonable method for aligning break curves and sherd outer surfaces simultaneously. In addition, a number of insights are given which have not previously been discussed and significantly reduce computation. Methods proposed for (1),(2), and (3) represent important contributions to the field of puzzle assembly, 3D geometry learning, and dataset alignment and are critical to making 3D puzzle solutions tractable to compute. Results are presented which include assembling a 13 sherd pot where only an incomplete set of 10 sherds is available.
An automatic assembly and completion framework for fragmented skulls
Authors: Zhao Yin, Li Wei, Mary Manhein, Xin Li 0003
Venue: Conference ICCV 2011
Year: 2011

We develop a completion pipeline for fragmented and damaged skulls. The goal of this work is to convert scanned incomplete skull fragments to a complete skull model for subsequent forensic or archeological tasks such as facial reconstruction. The proposed assembly and completion algorithms can also be used to repair other fragmented objects with inherent symmetry. A two-step assembly framework is proposed: (1) rough assembly by an ICP-like template matching algorithm integrated with the slippage features and spin-image descriptors; (2) assembly refinement by a global optimization on least square transformation error (LSTE) of break-curves. The assembled skull is finally repaired by a symmetry-based completion algorithm. Experiments on repairing scanned skull fragments demonstrate the efficacy and robustness of this framework.
Learned step size quantization
Authors: Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S. Modha
Venue: Conference ICLR 2020
Year: 2020

Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.
Intellilight: A reinforcement learning approach for intelligent traffic light control
Authors: Hua Wei, Guanjie Zheng, Huaxiu Yao, Zhenhui Li
Venue: Conference KDD 2018
Year: 2018

The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.
Learning phase competition for traffic signal control
Authors: Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li
Venue: Conference CIKM 2019
Year: 2019

Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportation efficiency is one of the most prominent topics. Recent studies have proposed to use reinforcement learning (RL) for traffic signal control. Different from traditional transportation approaches which rely heavily on prior knowledge, RL can learn directly from the feedback. On the other side, without a careful model design, existing RL methods typically take a long time to converge and the learned models may not be able to adapt to new scenarios. For example, a model that is trained well for morning traffic may not work for the afternoon traffic because the traffic flow could be reversed, resulting in a very different state representation. In this paper, we propose a novel design called FRAP, which is based on the intuitive principle of phase competition in traffic signal control: when two traffic signals conflict, priority should be given to one with larger traffic movement (i.e., higher demand). Through the phase competition modeling, our model achieves invariance to symmetrical cases such as flipping and rotation in traffic flow. By conducting comprehensive experiments, we demonstrate that our model finds better solutions than existing RL methods in the complicated all-phase selection problem, converges much faster during training, and achieves superior generalizability for different road structures and traffic conditions.
Dualde: Dually distilling knowledge graph embedding for faster and cheaper reasoning
Authors: Yushan Zhu, Wen Zhang, Mingyang Chen, Hui Chen, Xu Cheng, Wei Zhang, Huajun Chen
Venue: Conference WSDM 2022
Year: 2022

Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. However, high-dimensional KGEs pose huge challenges to storage and computing resources and are not suitable for resource-limited or time-constrained applications, for which faster and cheaper reasoning is necessary. To address this problem, we propose DualDE, a knowledge distillation method to build low-dimensional student KGE from pre-trained high-dimensional teacher KGE. DualDE considers the dual-influence between the teacher and the student. In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher. Our DualDE is general enough to be applied to various KGEs. Experimental results show that our method can successfully reduce the embedding parameters of a high-dimensional KGE by 7× - 15× and increase the inference speed by 2× - 6× while retaining a high performance. We also experimentally prove the effectiveness of our soft label evaluation mechanism and two-stage distillation approach via ablation study.
The anatomy of a news search engine
Authors: Antonio Gulli
Venue: Conference WWW 2005
Year: 2005

Today, news browsing and searching is one of the most important Internet activity. This paper introduces a general framework to build a News search engine by describing Velthune, an academic News search engine available on line.
Treatment effect estimation across domains
Authors: Yi-Xuan Sun, Ya-Lin Zhang, Wei Wang, Longfei Li, Jun Zhou
Venue: Conference CIKM 2023
Year: 2023

Treatment effect estimation is essential in the causal inference literature, which has attracted increasing attention in recent years. Most previous methods assume that the training and test data are drawn from the same distribution, which may not hold in practice since the effect estimators may need to be deployed across domains. Meanwhile, in real-world applications, little or no targeted treatments may be conducted in the new domain. Therefore, we focus on a more realistic scenario in this paper, where treatments and outcomes can be observed in the source domain, but the target domain only contains some unlabeled data, i.e., only features are available. In this scenario, thedistribution shift exists not only in the source data due to the selection bias between the control and treated groups, but also between the source and target data. We propose a novel direct learning framework along with the distribution adaptation and reliable scoring modules. In the distribution adaptation module, we design three specialized density ratio estimators to aid the issue of complex distribution shifts. Even so, we may face the challenge of unreliable pseudo-effects in this framework. To address that, we also design the uncertainty-based reliable scoring module as a vital support, which makes the method more reliable. The experiments are conducted on synthetic data and benchmark datasets, which demonstrate the superiority of our method.
Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue
Authors: Wenjie Wang 0007, Fuli Feng, Xiangnan He 0001, Hanwang Zhang, Tat-Seng Chua
Venue: Conference SIGIR 2021
Year: 2021

Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become the gold standard for building personalized recommendation service. However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item. This will severely hurt user's trust on the system if the user finds the actual content of the clicked item disappointing. What's even worse, optimizing CTR models on such flawed data will result in the Matthew Effect, making the seemingly attractive but actually low-quality items be more frequently recommended. In this paper, we formulate the recommendation models as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph. We imagine a counterfactual world where each item has only exposure features (i.e., the features that the user can see before making a click decision). By estimating the click likelihood of a user in the counterfactual world, we are able to reduce the direct effect of exposure features and eliminate the clickbait issue. Experiments on real-world datasets demonstrate that our method significantly improves the post-click satisfaction of CTR models.
Disentangled graph collaborative filtering
Authors: Xiang Wang 0010, Hongye Jin, An Zhang 0003, Xiangnan He 0001, Tong Xu 0001, Tat-Seng Chua
Venue: Conference SIGIR 2020
Year: 2020

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering.
Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings
Authors: Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi
Venue: Conference WSDM 2021
Year: 2021

Recommendation datasets are prone to selection biases due to self-selection behavior of users and item selection process of systems. This makes explicitly combating selection biases an essential problem in training recommender systems. Most previous studies assume no unbiased data available for training. We relax this assumption and assume that a small subset of training data is unbiased. Then, we propose a novel objective that utilizes the unbiased data to adaptively assign propensity weights to biased training ratings. This objective, combined with unbiased performance estimators, alleviates the effects of selection biases on the training of recommender systems. To optimize the objective, we propose an efficient algorithm that minimizes the variance of propensity estimates for better generalized recommender systems. Extensive experiments on two real-world datasets confirm the advantages of our approach in significantly reducing both the error of rating prediction and the variance of propensity estimation.
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
Authors: Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, Xiangnan He
Venue: Conference KDD 2021
Year: 2021

The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with pointwise or pairwise loss, makes the model biased towards popular items. This results in the terrible Matthew effect, making popular items be more frequently recommended and become even more popular. Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items. Although theoretically sound, IPW methods are highly sensitive to the weighting strategy, which is notoriously difficult to tune. In this work, we explore the popularity bias issue from a novel and fundamental perspective --- cause-effect. We identify that popularity bias lies in the direct effect from the item node to the ranking score, such that an item's intrinsic property is the cause of mistakenly assigning it a higher ranking score. To eliminate popularity bias, it is essential to answer the counterfactual question that what the ranking score would be if the model only uses item property. To this end, we formulate a causal graph to describe the important cause-effect relations in the recommendation process. During training, we perform multi-task learning to achieve the contribution of each cause; during testing, we perform counterfactual inference to remove the effect of item popularity. Remarkably, our solution amends the learning process of recommendation which is agnostic to a wide range of models --- it can be easily implemented in existing methods. We demonstrate it on Matrix Factorization (MF) and LightGCN [20], which are representative of the conventional and SOTA model for collaborative filtering. Experiments on five real-world datasets demonstrate the effectiveness of our method.
Leveraging Watch-Time Feedback for Short-Video Recommendations: A Causal Labeling Framework
Authors: Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang Song
Venue: Conference CIKM 2023
Year: 2023

With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiased Multiple-semantics-extracting Labeling (DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby directly mitigating bias at the label level. We substantiate the effectiveness of our DML framework through both online and offline experiments. Extensive results demonstrate that our DML could effectively leverage watch time to discover users' real interests, enhancing their engagement in our application.
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
Authors: Lingjie Mei, Jiayuan Mao, Ziqi Wang, Chuang Gan 0001, Joshua B. Tenenbaum
Venue: Conference ICLR 2022
Year: 2022

We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at images, reading sentences that describe the objects in the scene, and interpreting supplemental sentences that relate the novel concept with other concepts. The learned concepts support downstream applications, such as answering questions by reasoning about unseen images. Our model, namely FALCON, represents individual visual concepts, such as colors and shapes, as axis-aligned boxes in a high-dimensional space (the "box embedding space"). Given an input image and its paired sentence, our model first resolves the referential expression in the sentence and associates the novel concept with particular objects in the scene. Next, our model interprets supplemental sentences to relate the novel concept with other known concepts, such as "X has property Y" or "X is a kind of Y". Finally, it infers an optimal box embedding for the novel concept that jointly 1) maximizes the likelihood of the observed instances in the image, and 2) satisfies the relationships between the novel concepts and the known ones. We demonstrate the effectiveness of our model on both synthetic and real-world datasets.
Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Authors: Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
Venue: Conference ICLR 2023
Year: 2023

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy -- an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.
Dr. can see: towards a multi-modal disease diagnosis virtual assistant
Authors: Abhisek Tiwari, Manisimha Manthena, Sriparna Saha, Pushpak Bhattacharyya, Minakshi Dhar, Sarbajeet Tiwari
Venue: Conference CIKM 2022
Year: 2022

Artificial Intelligence-based clinical decision support is gaining ever-growing popularity and demand in both the research and industry communities. One such manifestation is automatic disease diagnosis, which aims to assist clinicians in conducting symptom investigations and disease diagnoses. When we consult with doctors, we often report and describe our health conditions with visual aids. Moreover, many people are unacquainted with several symptoms and medical terms, such as mouth ulcer and skin growth. Therefore, visual form of symptom reporting is a necessity. Motivated by the efficacy of visual form of symptom reporting, we propose and build a novel end-to-end Multi-modal Disease Diagnosis Virtual Assistant (MDD-VA) using reinforcement learning technique. In conversation, users' responses are heavily influenced by the ongoing dialogue context, and multi-modal responses appear to be of no difference. We also propose and incorporate a Context-aware Symptom Image Identification module that leverages discourse context in addition to the symptom image for identifying symptoms effectively. Furthermore, we first curate a multi-modal conversational medical dialogue corpus in English that is annotated with intent, symptoms, and visual information. The proposed MDD-VA outperforms multiple uni-modal baselines in both automatic and human evaluation, which firmly establishes the critical role of symptom information provided by visuals . The dataset and code are available at https://github.com/NLP-RL/DrCanSee