33,305 research outputs found
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Post-hoc model-agnostic interpretation methods such as partial dependence
plots can be employed to interpret complex machine learning models. While these
interpretation methods can be applied regardless of model complexity, they can
produce misleading and verbose results if the model is too complex, especially
w.r.t. feature interactions. To quantify the complexity of arbitrary machine
learning models, we propose model-agnostic complexity measures based on
functional decomposition: number of features used, interaction strength and
main effect complexity. We show that post-hoc interpretation of models that
minimize the three measures is more reliable and compact. Furthermore, we
demonstrate the application of these measures in a multi-objective optimization
approach which simultaneously minimizes loss and complexity
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
It has been increasingly demanding to develop reliable methods to evaluate
the progress of Graph Neural Network (GNN) research for molecular
representation learning. Existing GNN benchmarking methods for molecular
representation learning focus on comparing the GNNs' performances on some
node/graph classification/regression tasks on certain datasets. However, there
lacks a principled, task-agnostic method to directly compare two GNNs.
Additionally, most of the existing self-supervised learning works incorporate
handcrafted augmentations to the data, which has several severe difficulties to
be applied on graphs due to their unique characteristics. To address the
aforementioned issues, we propose GraphAC (Graph Adversarial Collaboration) --
a conceptually novel, principled, task-agnostic, and stable framework for
evaluating GNNs through contrastive self-supervision. We introduce a novel
objective function: the Competitive Barlow Twins, that allow two GNNs to
jointly update themselves from direct competitions against each other. GraphAC
succeeds in distinguishing GNNs of different expressiveness across various
aspects, and has demonstrated to be a principled and reliable GNN evaluation
method, without necessitating any augmentations.Comment: 11th International Conference on Learning Representations (ICLR 2023)
Machine Learning for Drug Discovery (MLDD) Workshop. 17 pages, 6 figures, 4
table
A Topic-Agnostic Approach for Identifying Fake News Pages
Fake news and misinformation have been increasingly used to manipulate
popular opinion and influence political processes. To better understand fake
news, how they are propagated, and how to counter their effect, it is necessary
to first identify them. Recently, approaches have been proposed to
automatically classify articles as fake based on their content. An important
challenge for these approaches comes from the dynamic nature of news: as new
political events are covered, topics and discourse constantly change and thus,
a classifier trained using content from articles published at a given time is
likely to become ineffective in the future. To address this challenge, we
propose a topic-agnostic (TAG) classification strategy that uses linguistic and
web-markup features to identify fake news pages. We report experimental results
using multiple data sets which show that our approach attains high accuracy in
the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019
World Wide Web Conference (WWW'19 Companion). Presented in the 2019
International Workshop on Misinformation, Computational Fact-Checking and
Credible Web (MisinfoWorkshop2019). 6 page
Efficient Learning with Arbitrary Covariate Shift
We give an efficient algorithm for learning a binary function in a given
class C of bounded VC dimension, with training data distributed according to P
and test data according to Q, where P and Q may be arbitrary distributions over
X. This is the generic form of what is called covariate shift, which is
impossible in general as arbitrary P and Q may not even overlap. However,
recently guarantees were given in a model called PQ-learning (Goldwasser et
al., 2020) where the learner has: (a) access to unlabeled test examples from Q
(in addition to labeled samples from P, i.e., semi-supervised learning); and
(b) the option to reject any example and abstain from classifying it (i.e.,
selective classification). The algorithm of Goldwasser et al. (2020) requires
an (agnostic) noise tolerant learner for C. The present work gives a
polynomial-time PQ-learning algorithm that uses an oracle to a "reliable"
learner for C, where reliable learning (Kalai et al., 2012) is a model of
learning with one-sided noise. Furthermore, our reduction is optimal in the
sense that we show the equivalence of reliable and PQ learning
Towards robust and reliable multimedia analysis through semantic integration of services
Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available
Selectively inhibiting learning bias for active sampling
Efficient training of machine learning algorithms requires a reliable labeled set from the application domain. Usually, data labeling is a costly process. Therefore, a selective approach is desirable. Active learning has been successfully used to reduce the labeling effort, due to its parsimonious process of querying the labeler. Nevertheless, many active learning strategies are dependent on early predictions made by learning algorithms. This might be a major problem when the learner is still unable to provide reliable information. In this context, agnostic strategies can be convenient, since they spare internal learners - usually favoring exploratory queries. On the other hand, prospective queries could benefit from a learning bias. In this article, we highlight the advantages of the agnostic approach and propose how to explore some of them without foregoing prospection. A simple hybrid strategy and a visualization tool called ranking curves, are proposed as a proof of concept. The tool allowed to see clearly when the presence of a learner was possibly detrimental. Finally, the hybrid strategy was successfully compared to its counterpart in the literature, to pure agnostic strategies and to the usual baseline of the field.CAPESCNPqFAPES
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