& Computer

By A. Yavuz. 2013

Subfields: Computer Graphics and Computer-Aided Design

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In Computer Graphics and Computer-Aided Design

Citations in the corpus

Inverse Reinforcement Learning in Swarm Systems (2016, methodology): “In particular, we make the following contributions: 1) We introduce the swarMDP , a formal framework to compactly describe homogeneous multi-agent control problems.”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “down to gritty details of architecture, algorithm, [and] code,” as these are essential to “explaining the social, ethical, and political dimensions of information technologies” [Nissenbaum, 2001].”
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, background): “Matrix factorization (MF) is a class of CF methods which learn user latent factors and item latent factors by factorizing their interaction matrix [Salakhutdinov and Mnih, 2007; Koren et al., 2009].”
Translation-based Recommendation (2017, methodology): “This includes Matrix Factorization (MF) techniques [ Koren et al. , 2009 ] that make use of inner products to model the compatibility be-tween user-item pairs (i.e., user preferences).”
Continual Learning of Large Language Models: A Comprehensive Survey (2024, unknown): “1 summarizes the existing studies on CPT, and here are some key observations we make about CPT.”
Quantum computations on a topologically encoded qubit (2014, methodology): “Ultimately, reliable quantum memories and arbitrarily long quantum computations are predicted to become feasible for appropriately designed quantum codes, once all elementary operations are realized in a fault-tolerant way and with suﬃciently low error rates [6, 7].”
Cross-Domain Recommendation: An Embedding and Mapping Approach (2017, methodology): “In this paper, two different models, namely MF [Koren et al., 2009] and BPR [Rendle et al., 2009], are employed in the framework, offering us a rating-oriented recommendation algorithm and a ranking-oriented one.”
Interpretable Drug Target Prediction Using Deep Neural Representation (2018, methodology): “From this perspective, matrix factorization has been widely used and proved effective [Koren et al. , 2009].”
Secure Federated Matrix Factorization (2019, methodology): “Stochastic gradient descent iteratively updates U and V with the following equations [Koren et al. , 2009]: Algorithm 1 User-level Distributed Matrix Factorization Init : Server initializes item proﬁle matrix V Init : User initializes user proﬁle matrix U Output : Converged U and V Server keeps…”
What to Do Next: Modeling User Behaviors by Time-LSTM (2017, methodology): “Most existing algorithms in RS, e.g. BPR (Bayesian Person-alized Ranking) [Rendle et al. , 2009], matrix factorization [Koren et al. , 2009], tensor models [Zhao et al. , 2015], focus on modeling users’ long-term interest, while the short-term interest seems to play a minor role in RS research.”
Understanding the Long-Term Use of Smart Speaker Assistants (2018, background): “Smart speaker devices represent a vision of pervasive computing initially laid out in research agendas around Calm Computing [24] and demonstrated in non-functional concept videos such as the Knowledge Navigator [1] from Apple in 1987.”
Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews (2016, methodology): “There has been significant amount of work focused on providing accurate recommendations based on the historical ratings [Pazzani and Billsus, 2007; Sarwar et al., 2001; Bell and Koren, 2007; Noh et al., 2004; Koren et al., 2009; Lops et al., 2011].”
Emerging energy applications of two-dimensional layered transition metal dichalcogenides (2015, background): “for deep submicron process technology nodes (<50 nm), more than 40% of the total energy is consumed due to the leakage of switching component.[89] Depending on”