September 19, 2020 AUTONOMOUS DRIVING, Machine Learning Leave a comment

Domain-invariant Similarity Activation Map Metric Learning for Retrieval-based Long-term Visual Localization

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods… Due to the drastic variability of environmental conditions, e.g.Su illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) […]

September 19, 2020 Machine Learning, QUESTION GENERATION Leave a comment

Asking Complex Questions with Multi-hop Answer-focused Reasoning

Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question words and copy relevant words from the input to the question. However, most state-of-the-art methods focus on asking simple questions involving single-hop relations… In this paper, we propose a new task called multihop question generation that asks complex and semantically relevant questions by additionally discovering and modeling the multiple entities and their semantic relations given […]

September 19, 2020 KNOWLEDGE GRAPH COMPLETION, Machine Learning Leave a comment

CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false… To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each […]

September 19, 2020 ADVERSARIAL ATTACK, Machine Learning Leave a comment

Contextualized Perturbation for Textual Adversarial Attack

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs… This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. CLARE builds on a pre-trained masked language model and modifies the inputs in […]

September 19, 2020 Machine Learning, TEXT GENERATION Leave a comment

UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for open-ended text generation including story or dialog generation because of the notorious one-to-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references. To alleviate this issue, we propose UNION, a learnable unreferenced metric for evaluating open-ended story generation, which measures the quality of a generated story […]

September 19, 2020 IMAGE SUPER-RESOLUTION, Machine Learning Leave a comment

Parallax Attention for Unsupervised Stereo Correspondence Learning

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities… However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we […]

September 19, 2020 INFORMATION RETRIEVAL, Machine Learning Leave a comment

Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation

Annotating music items with music genres is crucial for music recommendation and information retrieval, yet challenging given that music genres are subjective concepts. Recently, in order to explicitly consider this subjectivity, the annotation of music items was modeled as a translation task: predict for a music item its music genres within a target vocabulary or taxonomy (tag system) from a set of music genre tags originating from other tag systems… However, without a parallel corpus, previous solutions could not handle […]

September 19, 2020 ANOMALY DETECTION, Machine Learning Leave a comment

Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts or domain experts will be employed to investigate the top instances one by one in a ranked list of anomalies identified by an anomaly detection system… This verification procedure generates informative labels that can be leveraged to re-rank the anomalies so as to help the analyst to discover more true anomalies given a time budget. Some re-ranking […]

September 18, 2020 Machine Learning, RECOMMENDATION SYSTEMS Leave a comment

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

Cross domain recommendation (CDR) has been proposed to tackle the data sparsity problem in recommender systems. This paper focuses on a common scenario for CDR where different domains share the same set of users but no overlapping items… The majority of recent methods have explored shared-user representation to transfer knowledge across different domains. However, the idea of shared-user representation resorts to learn the overlapped properties of user preferences across different domains and suppresses the domain-specific properties of user preferences. In […]

September 18, 2020 Machine Learning Leave a comment

Soft policy optimization using dual-track advantage estimator

In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory. Based on this principle, in this paper, we soften the proximal policy optimization by introducing the entropy and dynamically setting the temperature coefficient to balance the opportunity of exploration and exploitation… While maximizing the expected reward, the agent will also seek other trajectories […]

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