1,413 research outputs found
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Recurrent Latent Variable Networks for Session-Based Recommendation
In this work, we attempt to ameliorate the impact of data sparsity in the
context of session-based recommendation. Specifically, we seek to devise a
machine learning mechanism capable of extracting subtle and complex underlying
temporal dynamics in the observed session data, so as to inform the
recommendation algorithm. To this end, we improve upon systems that utilize
deep learning techniques with recurrently connected units; we do so by adopting
concepts from the field of Bayesian statistics, namely variational inference.
Our proposed approach consists in treating the network recurrent units as
stochastic latent variables with a prior distribution imposed over them. On
this basis, we proceed to infer corresponding posteriors; these can be used for
prediction and recommendation generation, in a way that accounts for the
uncertainty in the available sparse training data. To allow for our approach to
easily scale to large real-world datasets, we perform inference under an
approximate amortized variational inference (AVI) setup, whereby the learned
posteriors are parameterized via (conventional) neural networks. We perform an
extensive experimental evaluation of our approach using challenging benchmark
datasets, and illustrate its superiority over existing state-of-the-art
techniques
Hamstrings co-activation in ACL-deficient subjects during isometric whole-leg extensions
It has been reported that anterior cruciate ligament (ACL)-deficient subjects increase the level of hamstrings activation and this has been interpreted as a means to cope with increased anterior tibial laxity in the knee. This study aimed to establish to what extent co-activation strategies in ACL-deficient subjects are load level and knee angle dependent. Eleven chronic ACL-deficient and 15 control subjects were positioned in a range of postures and asked to exert a feedback controlled vertical ground reaction force (GRF; 30, 60% and maximum), while horizontal forces were not constrained. Surface electromyography of the leg muscles and GRF were measured. In postures with the knee over and in front of the ankle, ACL-deficient subjects generated, respectively, 2.4 and 5.1% MVC more hamstrings activation than control subjects. Enhanced hamstrings co-activation in ACL-deficient subjects was more apparent in extended than in flexed knee angles. For both ACL-deficient and control subjects, hamstrings co-activation was larger in males than in females. It is concluded that ACL-deficient subjects show a task dependent increase in hamstrings co-activation, but its clinical significance remains to be shown. © Springer-Verlag 2009
The cost of prospecting for dispersal opportunities in a social bird
Understanding why individuals delay dispersal and become subordinates within a group is central to studying the evolution of sociality. Hypotheses predict that dispersal decisions are influenced by costs of extra-territorial prospecting that are often required to find a breeding vacancy. Little is known about such costs, partly because it is complicated to demonstrate them empirically. For example, prospecting individuals may be of inferior quality already before prospecting and/or have been evicted. Moreover, costs of prospecting are mainly studied in species where prospectors suffer from predation risk, so how costly prospecting is when predators are absent remains unclear. Here, we determine a cost of prospecting for subordinate Seychelles warblers, Acrocephalus sechellensis, in a population where predators are absent and individuals return to their resident territory after prospecting. Prospecting individuals had 5.2% lower body mass than non-prospecting individuals. Our evidence suggests this may be owing to frequent attacks by resident conspecifics, likely leading to reduced food intake by prospectors. These results support the hypothesis that energetic costs associated with dispersal opportunities are one factor influencing dispersal decisions and shaping the evolution of delayed dispersal in social animals
Where are we now? A large benchmark study of recent symbolic regression methods
In this paper we provide a broad benchmarking of recent genetic programming
approaches to symbolic regression in the context of state of the art machine
learning approaches. We use a set of nearly 100 regression benchmark problems
culled from open source repositories across the web. We conduct a rigorous
benchmarking of four recent symbolic regression approaches as well as nine
machine learning approaches from scikit-learn. The results suggest that
symbolic regression performs strongly compared to state-of-the-art gradient
boosting algorithms, although in terms of running times is among the slowest of
the available methodologies. We discuss the results in detail and point to
future research directions that may allow symbolic regression to gain wider
adoption in the machine learning community.Comment: 8 pages, 4 figures. GECCO 201
First principles calculation of vibrational Raman spectra in large systems: signature of small rings in crystalline SiO2
We present an approach for the efficient calculation of vibrational Raman
intensities in periodic systems within density functional theory. The Raman
intensities are computed from the second order derivative of the electronic
density matrix with respect to a uniform electric field. In contrast to
previous approaches, the computational effort required by our method for the
evaluation of the intensities is negligible compared to that required for the
calculation of vibrational frequencies. As a first application, we study the
signature of 3- and 4-membered rings in the the Raman spectra of several
polymorphs of SiO2, including a zeolite having 102 atoms per unit cell.Comment: 4 pages, 2 figures, revtex4 Minor corrections; accepted in Phys. Rev.
Let
Neural Attentive Session-based Recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short
sessions. Previous work only considers the user's sequential behavior in the
current session, whereas the user's main purpose in the current session is not
emphasized. In this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle this problem.
Specifically, we explore a hybrid encoder with an attention mechanism to model
the user's sequential behavior and capture the user's main purpose in the
current session, which are combined as a unified session representation later.
We then compute the recommendation scores for each candidate item with a
bi-linear matching scheme based on this unified session representation. We
train NARM by jointly learning the item and session representations as well as
their matchings. We carried out extensive experiments on two benchmark
datasets. Our experimental results show that NARM outperforms state-of-the-art
baselines on both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its advantages in
modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939,
arXiv:1606.08117 by other author
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
The cross-domain recommendation technique is an effective way of alleviating
the data sparse issue in recommender systems by leveraging the knowledge from
relevant domains. Transfer learning is a class of algorithms underlying these
techniques. In this paper, we propose a novel transfer learning approach for
cross-domain recommendation by using neural networks as the base model. In
contrast to the matrix factorization based cross-domain techniques, our method
is deep transfer learning, which can learn complex user-item interaction
relationships. We assume that hidden layers in two base networks are connected
by cross mappings, leading to the collaborative cross networks (CoNet). CoNet
enables dual knowledge transfer across domains by introducing cross connections
from one base network to another and vice versa. CoNet is achieved in
multi-layer feedforward networks by adding dual connections and joint loss
functions, which can be trained efficiently by back-propagation. The proposed
model is thoroughly evaluated on two large real-world datasets. It outperforms
baselines by relative improvements of 7.84\% in NDCG. We demonstrate the
necessity of adaptively selecting representations to transfer. Our model can
reduce tens of thousands training examples comparing with non-transfer methods
and still has the competitive performance with them.Comment: Deep transfer learning for recommender system
Delayed dispersal and the cost and benefits of different routes to independent breeding in a cooperative breeding bird
Why sexually mature individuals stay in groups as non-reproductive subordinates is central to the evolution of sociality and cooperative breeding. To understand such delayed dispersal, its costs and benefits need to be compared with those of permanently leaving to float through the population. However, comprehensive comparisons, especially regarding differences in future breeding opportunities, are rare. Moreover, extra-territorial prospecting by philopatric individuals has generally been ignored, even though the factors underlying this route to independent breeding may differ from those of strict philopatry or floating. We use a comprehensive predictive framework to explore how various costs, benefits and intrinsic, environmental and social factors explain philopatry, prospecting and floating in Seychelles warblers (Acrocephalus sechellensis). Floaters more likely obtained an independent breeding position before the next season than strictly philopatric individuals, but also suffered higher mortality. Prospecting yielded similar benefits to floating but lower mortality costs, suggesting that it is overall more beneficial than floating and strict philopatry. Whereas prospecting is probably individual-driven, though limited by resource availability, floating likely results from eviction by unrelated breeders. Such differences in proximate and ultimate factors underlying each route to independent breeding highlight the need for simultaneous consideration when studying the evolution of delayed dispersal
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