paper_id stringlengths 10 10 | published stringdate 2016-01-01 00:00:00 2026-10-29 00:00:00 | year int16 2.02k 2.03k | title stringlengths 4 240 | n_authors int32 1 3.44k | primary_category stringclasses 148
values | categories listlengths 1 9 | answered bool 2
classes | affiliations_on_page bool 2
classes | subfield stringclasses 20
values | subfield_2 stringclasses 19
values | keywords listlengths 0 14 ⌀ | keywords_printed bool 2
classes | venue stringclasses 18
values | venue_year int16 2.02k 2.03k ⌀ | track stringclasses 4
values | main_conference bool 2
classes | code_or_project_url stringlengths 1 171 ⌀ | code bool 2
classes | s2_paper_id stringlengths 40 40 ⌀ | citations int64 0 199k ⌀ | influential_citations int64 0 23.2k ⌀ | highly_cited float64 0 1 ⌀ | highly_cited_threshold int64 0 1.92k ⌀ | independent bool 2
classes | credit_placed float64 0 1 ⌀ | countries listlengths 0 29 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1601.00022 | 2016-01-05 | 2,016 | Event Specific Multimodal Pattern Mining with Image-Caption Pairs | 3 | cs.CV | [
"cs.CV"
] | true | true | multimodal | null | [
"visual pattern mining",
"image-caption pairs",
"multimodal learning",
"news events",
"weakly supervised learning"
] | false | null | null | null | false | null | false | bc19b2745f89b5d511da745c7c5b3d16bca2d0cf | 2 | 0 | 0 | 659 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00024 | 2016-01-01 | 2,016 | Selecting Near-Optimal Learners via Incremental Data Allocation | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | core_ml | optimization | [
"data allocation",
"model selection",
"hyper-parameter optimization",
"upper confidence bounds",
"regret bounds",
"learner selection"
] | false | AAAI | 2,016 | main | true | null | false | f6d29f22c70ff50f0598d1bf4c5556e9463d336c | 66 | 1 | 0 | 245 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00025 | 2016-01-01 | 2,016 | Write a Classifier: Predicting Visual Classifiers from Unstructured Text | 3 | cs.CV | [
"cs.CV",
"cs.CL",
"cs.LG"
] | true | false | multimodal | vision | [
"zero-shot learning",
"visual classifiers",
"unstructured text",
"fine-grained categorization",
"kernel methods",
"distributional semantics"
] | true | null | null | null | false | null | false | 7f45650e4c9dd8cbc2bf2dd411fc24ba5631de60 | 42 | 4 | 0 | 659 | false | 0 | [] |
1601.00027 | 2016-01-01 | 2,016 | Computational Pathology: Challenges and Promises for Tissue Analysis | 2 | cs.CV | [
"cs.CV",
"cs.AI"
] | true | true | health | vision | [
"Computational Pathology",
"Machine Learning",
"Medical Imaging",
"Survival Statistics",
"Cancer Research"
] | true | null | null | null | false | null | false | f5296805af309c7c98027bddc7b3ba57ed701271 | 279 | 4 | 1 | 242 | false | 1 | [
{
"country": "CH",
"credit": 1
}
] |
1601.00034 | 2016-01-01 | 2,016 | Stochastic Neural Networks with Monotonic Activation Functions | 5 | stat.ML | [
"stat.ML",
"cs.LG",
"cs.NE"
] | true | true | core_ml | null | [
"restricted boltzmann machines",
"stochastic neurons",
"monotonic activation functions",
"laplace approximation",
"contrastive divergence",
"exponential family harmoniums"
] | false | null | null | null | false | null | false | 52fd1b061d19248c9213b39632f9fca5d41a33bc | 21 | 2 | 0 | 245 | false | 1 | [
{
"country": "US",
"credit": 0.6000000000000001
},
{
"country": "CA",
"credit": 0.4
}
] |
1601.00062 | 2016-01-01 | 2,016 | Practical Algorithms for Learning Near-Isometric Linear Embeddings | 5 | stat.ML | [
"stat.ML",
"cs.LG",
"math.OC"
] | true | true | optimization | null | [
"dimensionality reduction",
"linear embeddings",
"compressive sensing",
"approximate nearest neighbors",
"classification"
] | true | null | null | null | false | null | false | d3d85057a5bf8666910105c72b0108ef9730652d | 4 | 0 | 0 | 143 | false | 1 | [
{
"country": "US",
"credit": 0.8
},
{
"country": "GB",
"credit": 0.2
}
] |
1601.00072 | 2016-01-01 | 2,016 | GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation | 3 | cs.DC | [
"cs.DC",
"cs.CV"
] | true | true | systems | health | [
"fuzzy c-means",
"gpu",
"cuda",
"image segmentation",
"parallel algorithms"
] | false | null | null | null | false | null | false | ff30f2e300f5264fe8837412b59e1d8ea51b6035 | 12 | 2 | 0 | 347 | false | 1 | [
{
"country": "MY",
"credit": 0.9999999999999999
}
] |
1601.00087 | 2016-01-01 | 2,016 | Sentiment/Subjectivity Analysis Survey for Languages other than English | 3 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"sentiment analysis",
"subjectivity analysis",
"non-English languages",
"cross-lingual transfer",
"Arabic sentiment analysis",
"survey"
] | false | null | null | null | false | null | false | 11e589e30e8c8fc0ae53cf31ec35918a7da6d5eb | 33 | 1 | 0 | 320 | false | 0.333333 | [
{
"country": "US",
"credit": 0.3333333333333333
}
] |
1601.00088 | 2016-01-01 | 2,016 | Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective | 3 | cs.CV | [
"cs.CV"
] | true | true | vision | probabilistic | [
"non-local means",
"patch-based filtering",
"Sinkhorn-Knopp",
"Gaussian mixture model",
"expectation-maximization",
"image denoising"
] | true | null | null | null | false | http://engineering.purdue.edu/ChanGroup/ | false | 8965ddbeab8f45778766889e12151d80718b421e | 10 | 0 | 0 | 499 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00119 | 2016-01-01 | 2,016 | Discriminative Sparsity for Sonar ATR | 4 | cs.CV | [
"cs.CV"
] | true | true | vision | speech_audio | [
"sonar automatic target recognition",
"sparse representation classification",
"discriminative sparsity",
"noisy sonar imagery",
"limited training samples",
"out-of-class detection"
] | false | null | null | null | false | null | false | 0d1dc826397111174290e24a71dcadaf2f07f469 | 6 | 0 | 0 | 499 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00142 | 2016-01-02 | 2,016 | Joint Estimation of Precision Matrices in Heterogeneous Populations | 2 | stat.ML | [
"stat.ML"
] | true | true | probabilistic | health | [
"hierarchical clustering",
"graph laplacian",
"high-dimensional estimation",
"precision matrix",
"heterogeneous populations",
"sparsity"
] | true | null | null | null | false | null | false | 8b670bccb1550144f5a24917d5ad2d5134cf28cd | 59 | 5 | 0 | 129 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00149 | 2016-01-02 | 2,016 | Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data | 6 | cs.CV | [
"cs.CV"
] | true | true | core_ml | null | [
"tensor sparse representation",
"low-rank representation",
"subspace clustering",
"submodule clustering",
"multi-way data",
"t-product"
] | false | null | null | null | false | null | false | 4c652663fc9e3e67bf08088d8153e35d741a33ce | 14 | 5 | 0 | 245 | false | 0.833333 | [
{
"country": "CN",
"credit": 0.6666666666666666
},
{
"country": "AU",
"credit": 0.16666666666666666
}
] |
1601.00191 | 2016-01-02 | 2,016 | An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms | 1 | cs.NE | [
"cs.NE"
] | true | true | core_ml | null | [
"Approximate Computing",
"Associative Learning",
"Cortical Learning Algorithms",
"Embedded Systems",
"Hierarchical Temporal Memory",
"Intelligent Agent"
] | true | null | null | null | false | null | false | 95d25efa330e33b4643ee381b4bef3d44dfdf1d1 | 2 | 0 | 0 | 245 | false | 1 | [
{
"country": "NG",
"credit": 1
}
] |
1601.00199 | 2016-01-02 | 2,016 | A Unified Framework for Compositional Fitting of Active Appearance Models | 2 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"active appearance models",
"compositional gradient descent",
"non-linear optimization",
"bayesian inference",
"schur complement",
"wiberg algorithm"
] | true | null | null | null | false | http://www.menpo.org | false | a09c10b198cc98e8d4958da4963ae26a2207dbfc | 38 | 4 | 0 | 499 | false | 1 | [
{
"country": "GB",
"credit": 1
}
] |
1601.00210 | 2016-01-02 | 2,016 | Susceptibility of texture measures to noise: an application to lung tumor CT images | 2 | cs.CV | [
"cs.CV"
] | true | true | health | safety | [] | false | null | null | null | false | null | false | 284d5183523f64c694b5e2b510b0a3221f9528d7 | 10 | 0 | 0 | 242 | false | 1 | [
{
"country": "GB",
"credit": 1
}
] |
1601.00211 | 2016-01-02 | 2,016 | A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours | 1 | cs.CV | [
"cs.CV"
] | true | true | health | vision | [
"wavelet packet analysis",
"fractal dimension",
"meningioma",
"texture classification",
"histopathology",
"best basis selection"
] | true | null | null | null | false | null | false | 31ac3df93c8a262ec8f77625bf1907f8dbea62c7 | 19 | 2 | 0 | 242 | false | 1 | [
{
"country": "GB",
"credit": 1
}
] |
1601.00212 | 2016-01-02 | 2,016 | Supervised Texture Segmentation: A Comparative Study | 1 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"texture segmentation",
"Gabor filters",
"Gaussian Markov random fields",
"co-occurrence matrix",
"run-length matrix",
"Bayesian classification"
] | true | null | null | null | false | null | false | 3c310504cb160abf3d339ee2bf7663d4f71925b0 | 18 | 1 | 0 | 499 | false | 1 | [
{
"country": "JO",
"credit": 1
}
] |
1601.00236 | 2016-01-03 | 2,016 | Supervised Dimensionality Reduction via Distance Correlation Maximization | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | core_ml | null | [
"supervised dimensionality reduction",
"distance correlation",
"low-dimensional representation",
"nonlinear dependency",
"generalized minimization maximization"
] | false | null | null | null | false | null | false | 6f8152f71351895d7a5f7783545725bd51551646 | 41 | 2 | 0 | 245 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00238 | 2016-01-03 | 2,016 | Dimensionality-Dependent Generalization Bounds for $k$-Dimensional Coding Schemes | 3 | stat.ML | [
"stat.ML",
"cs.LG"
] | true | true | theory | null | [
"k-dimensional coding",
"generalization bounds",
"covering number",
"dictionary learning",
"sparse coding",
"vector quantization"
] | false | null | null | null | false | null | false | defdb21ed199387b5a92775c24459b556882ab3f | 58 | 2 | 0 | 144 | false | 1 | [
{
"country": "AU",
"credit": 0.6666666666666666
},
{
"country": "SG",
"credit": 0.3333333333333333
}
] |
1601.00248 | 2016-01-03 | 2,016 | Contrastive Entropy: A new evaluation metric for unnormalized language models | 2 | cs.CL | [
"cs.CL"
] | true | false | llm_nlp | null | [
"perplexity",
"language models",
"contrastive entropy",
"evaluation metric",
"unnormalized models",
"recurrent neural networks"
] | false | null | null | null | false | null | false | dd173120ef93257c6f1648b331a631911216ef56 | 7 | 0 | 0 | 320 | false | 0 | [] |
1601.00260 | 2016-01-03 | 2,016 | Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection | 3 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"super resolution",
"iterative back projection",
"image registration",
"interpolation",
"image enhancement"
] | true | null | null | null | false | null | false | 14102f2cdc553386be57d71120c9db139b3340ff | 28 | 1 | 0 | 499 | false | 1 | [
{
"country": "CY",
"credit": 1
}
] |
1601.00287 | 2016-01-03 | 2,016 | Wavelet Scattering on the Pitch Spiral | 2 | cs.SD | [
"cs.SD",
"cs.AI"
] | true | true | speech_audio | theory | [
"scattering transform",
"wavelets",
"pitch spiral",
"harmonic sounds",
"source-filter model",
"audio representation"
] | false | null | null | null | false | www.github.com/lostanlen/scattering.m | true | 1ed8d74530bf6c2b9eb56b22d28a317d4524e493 | 24 | 0 | 0 | 291 | false | 1 | [
{
"country": "FR",
"credit": 1
}
] |
1601.00311 | 2016-01-03 | 2,016 | How can one sample images with sampling rates close to the theoretical minimum? | 1 | cs.CV | [
"cs.CV",
"physics.optics"
] | true | true | other | null | [
"image sampling",
"image reconstruction",
"sampling theory",
"compressed sensing",
"sparse approximation",
"minimal sampling rate"
] | false | null | null | null | false | null | false | 492e4b8ec5da1d9f2135303d9602f26c5aa9d6a2 | 0 | 0 | 0 | 83 | false | 1 | [
{
"country": "IL",
"credit": 1
}
] |
1601.00313 | 2016-01-03 | 2,016 | When more of the same is better | 1 | cs.MA | [
"cs.MA",
"physics.soc-ph"
] | true | true | other | optimization | [
"collective intelligence",
"agent-based model",
"imitative learning",
"diversity",
"NK landscape",
"problem solving"
] | false | null | null | null | false | null | false | 5f048b03a6bbfbd831302d796e3e9194e9b288b3 | 13 | 0 | 0 | 83 | false | 1 | [
{
"country": "BR",
"credit": 1
}
] |
1601.00318 | 2016-01-03 | 2,016 | A Unified Approach for Learning the Parameters of Sum-Product Networks | 3 | cs.LG | [
"cs.LG",
"cs.AI"
] | true | true | probabilistic | optimization | [
"sum-product networks",
"parameter learning",
"maximum likelihood estimation",
"signomial program",
"concave-convex procedure",
"expectation maximization"
] | false | NeurIPS | 2,016 | main | true | null | false | 6f3b134b24f18c4a81c2716662a24af13241e526 | 80 | 11 | 0 | 129 | false | 0.666667 | [
{
"country": "US",
"credit": 0.3333333333333333
},
{
"country": "CA",
"credit": 0.3333333333333333
}
] |
1601.00350 | 2016-01-03 | 2,016 | Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks | 3 | stat.ML | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT"
] | true | true | systems | null | [
"one bit compressed sensing",
"wireless sensor networks",
"diffusion strategy",
"steepest descent",
"distributed learning",
"sparse recovery"
] | false | null | null | null | false | null | false | bde51f06da1ea29e9e085aa658fe25c64e735db6 | 0 | 0 | 0 | 347 | false | 1 | [
{
"country": "IR",
"credit": 0.6666666666666666
},
{
"country": "AU",
"credit": 0.3333333333333333
}
] |
1601.00367 | 2016-01-01 | 2,016 | Benders Decomposition for the Design of a Hub and Shuttle Public Transit System | 3 | cs.AI | [
"cs.AI"
] | true | true | optimization | null | [
"benders decomposition",
"hub and shuttle transit",
"multimodal network design",
"pareto optimal cuts",
"cut bundling",
"mixed integer programming"
] | true | null | null | null | false | null | false | 6a074b4f0543bfae11468592c9101bc12e0ba422 | 104 | 6 | 0 | 143 | false | 1 | [
{
"country": "AU",
"credit": 0.6666666666666666
},
{
"country": "US",
"credit": 0.3333333333333333
}
] |
1601.00372 | 2016-01-04 | 2,016 | Mutual Information and Diverse Decoding Improve Neural Machine Translation | 2 | cs.CL | [
"cs.CL",
"cs.AI"
] | true | true | llm_nlp | null | [
"neural machine translation",
"mutual information",
"sequence-to-sequence",
"diverse decoding",
"n-best re-ranking"
] | false | null | null | null | false | null | false | 1cc0c322af508a8f7b6ea9705c9023c78bc7bc6f | 130 | 14 | 0 | 320 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00393 | 2016-01-04 | 2,016 | On the Reducibility of Submodular Functions | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | optimization | null | [
"submodular functions",
"reducibility",
"perturbation",
"lattice reduction",
"submodular optimization",
"scalability"
] | false | null | null | null | false | null | false | 317ebd8727df3a57495141fb817f2414a6eba673 | 0 | 0 | 0 | 143 | false | 1 | [
{
"country": "CA",
"credit": 0.3333333333333333
},
{
"country": "US",
"credit": 0.3333333333333333
},
{
"country": "CN",
"credit": 0.3333333333333333
}
] |
1601.00396 | 2016-01-04 | 2,016 | Automatic Detection and Decoding of Photogrammetric Coded Targets | 3 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"photogrammetry",
"coded targets",
"target detection",
"decoding",
"close-range photogrammetry"
] | false | null | null | null | false | null | false | ee0c2a4096e8b43611deaf6684579123fa073b9c | 10 | 0 | 0 | 499 | false | 1 | [
{
"country": "KR",
"credit": 1
}
] |
1601.00400 | 2016-01-04 | 2,016 | Multi-task CNN Model for Attribute Prediction | 4 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"attribute prediction",
"multi-task learning",
"deep CNN",
"semantic attributes",
"latent task matrix"
] | true | null | null | null | false | null | false | f69cda1175c3ddf2d4e3f3789a6c743b1059ebb2 | 310 | 16 | 0 | 499 | false | 1 | [
{
"country": "SG",
"credit": 0.5
},
{
"country": "CN",
"credit": 0.5
}
] |
1601.00414 | 2016-01-04 | 2,016 | Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds | 5 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"subspace clustering",
"symmetric positive definite manifold",
"kernel methods",
"log-Euclidean kernel",
"sparse representation",
"Riemannian geometry"
] | false | CVPR | 2,016 | main | true | null | false | ea61e3786d0773649436ed17f96c8a263e8b3575 | 113 | 3 | 0 | 499 | false | 1 | [
{
"country": "CN",
"credit": 0.6000000000000001
},
{
"country": "AU",
"credit": 0.4
}
] |
1601.00449 | 2016-01-04 | 2,016 | Fitting Spectral Decay with the $k$-Support Norm | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | optimization | null | [
"k-support norm",
"orthogonally invariant norms",
"matrix completion",
"multitask learning",
"proximal point algorithms"
] | true | null | null | null | false | null | false | 586636a3709d32b45d02ccfa9656baa012ee69f2 | 12 | 3 | 0 | 143 | false | 1 | [
{
"country": "GB",
"credit": 0.5
},
{
"country": "IT",
"credit": 0.5
}
] |
1601.00496 | 2016-01-04 | 2,016 | Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data | 5 | stat.AP | [
"stat.AP",
"q-bio.NC",
"stat.ML"
] | true | true | health | probabilistic | [
"dynamic functional connectivity",
"Bayesian nonparametric modeling",
"hidden Markov modeling",
"Wishart mixture modeling",
"predictive likelihood"
] | true | null | null | null | false | null | false | 6f42211a3334602fbeac34747c92352806d90859 | 13 | 0 | 0 | 242 | false | 1 | [
{
"country": "DK",
"credit": 1
}
] |
1601.00504 | 2016-01-04 | 2,016 | Learning relationships between data obtained independently | 2 | stat.ML | [
"stat.ML"
] | true | true | probabilistic | null | [
"data fusion",
"statistical matching",
"quantile matching",
"deconvolution",
"monotone dependence"
] | false | null | null | null | false | null | false | af96b41d7a26edf107aedba6472070f74ad53fd6 | 28 | 8 | 0 | 129 | false | 1 | [
{
"country": "DE",
"credit": 0.75
},
{
"country": "GB",
"credit": 0.25
}
] |
1601.00529 | 2016-01-05 | 2,016 | Programming in logic without logic programming | 2 | cs.AI | [
"cs.AI"
] | true | true | other | null | [
"reactive rules",
"state transition systems",
"reactive models",
"model generation",
"KELPS",
"logic-based agents"
] | true | null | null | null | false | null | false | d8564aa0f5a23f69fb1d6ababdbdc2935616e302 | 21 | 0 | 0 | 83 | false | 1 | [
{
"country": "GB",
"credit": 1
}
] |
1601.00543 | 2016-01-04 | 2,016 | Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning | 6 | cs.IT | [
"cs.IT",
"cs.LG",
"math.IT"
] | true | true | core_ml | probabilistic | [
"compressed sensing",
"structured sparsity",
"approximate message passing",
"k-nearest neighbor"
] | true | null | null | null | false | null | false | 26c162b989bf06707db63e3a22b017a0ee74daf6 | 11 | 0 | 0 | 245 | false | 0.833333 | [
{
"country": "CN",
"credit": 0.6666666666666666
},
{
"country": "AU",
"credit": 0.16666666666666666
}
] |
1601.00574 | 2016-01-04 | 2,016 | NFL Play Prediction | 3 | cs.LG | [
"cs.LG"
] | true | true | other | null | [
"NFL play prediction",
"playcalling",
"sports analytics",
"regression",
"classification",
"progress measure"
] | false | null | null | null | false | null | false | cd6cb85e4a1ca3c141dac1725c8be0b512d77dd2 | 5 | 1 | 0 | 83 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00595 | 2016-01-04 | 2,016 | Robust Non-linear Regression: A Greedy Approach Employing Kernels with Application to Image Denoising | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | false | core_ml | vision | [
"robust regression",
"kernel methods",
"greedy algorithms",
"outlier detection",
"image denoising"
] | false | null | null | null | false | null | false | dcb39f2714cf2ef6e72cc656dc26af8a9dc8dbb7 | 12 | 0 | 0 | 245 | false | 1 | [
{
"country": "GR",
"credit": 1
}
] |
1601.00599 | 2016-01-04 | 2,016 | Multimodal Classification of Events in Social Media | 2 | cs.CV | [
"cs.CV",
"cs.IR",
"cs.MM"
] | true | true | multimodal | retrieval | [
"social events",
"social media retrieval",
"event classification",
"multimodal retrieval"
] | true | null | null | null | false | null | false | 6ab75a1fa1ab1c0c9c2c4d8536f2a05de434a418 | 24 | 1 | 0 | 659 | false | 1 | [
{
"country": "AT",
"credit": 1
}
] |
1601.00620 | 2016-01-04 | 2,016 | Distant IE by Bootstrapping Using Lists and Document Structure | 4 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"distant supervision",
"information extraction",
"coupling constraints",
"label propagation",
"document structure",
"noisy training data"
] | false | AAAI | 2,016 | main | true | null | false | 9a41111cf881b052555985bd8cf304ef9fc4f6d5 | 14 | 0 | 0 | 320 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00626 | 2016-01-04 | 2,016 | Scalable Models for Computing Hierarchies in Information Networks | 2 | cs.AI | [
"cs.AI",
"cs.DL",
"cs.LG"
] | true | true | probabilistic | graphs | [
"hierarchical topic model",
"information networks",
"nonparametric bayesian",
"vertex programming",
"taxonomy induction",
"web-scale learning"
] | false | null | null | null | false | null | false | 901bfaeda6d8aa54527bf26e72ee2f16ca06b3d4 | 2 | 0 | 0 | 129 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00669 | 2016-01-04 | 2,016 | Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches | 6 | cs.AI | [
"cs.AI"
] | true | true | agents | robotics | [
"cognitive architecture",
"computational creativity",
"dual process theory",
"Psi model",
"artificial painter",
"robotic creativity"
] | false | null | null | null | false | null | false | e8b362493d7bbc62ac320d622d4c567db018a0ac | 33 | 2 | 0 | 41 | false | 1 | [
{
"country": "IT",
"credit": 0.9999999999999999
}
] |
1601.00670 | 2016-01-04 | 2,016 | Variational Inference: A Review for Statisticians | 3 | stat.CO | [
"stat.CO",
"cs.LG",
"stat.ML"
] | true | true | probabilistic | null | [
"variational inference",
"approximate posterior",
"kullback-leibler divergence",
"mean-field",
"bayesian statistics",
"stochastic optimization"
] | true | null | null | null | false | null | false | 6f24d7a6e1c88828e18d16c6db20f5329f6a6827 | 6,033 | 561 | 1 | 129 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00701 | 2016-01-04 | 2,016 | Nonlinear Hebbian learning as a unifying principle in receptive field formation | 2 | q-bio.NC | [
"q-bio.NC",
"cs.LG"
] | true | true | core_ml | science | [
"nonlinear hebbian learning",
"receptive fields",
"natural image statistics",
"sparse coding",
"synaptic plasticity",
"sensory coding"
] | false | null | null | null | false | null | false | 35475d771ee4be42e37f7d49e65455b9c3d6d5c4 | 71 | 3 | 0 | 245 | false | 1 | [
{
"country": "CH",
"credit": 1
}
] |
1601.00706 | 2016-01-05 | 2,016 | Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis | 4 | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CV"
] | true | true | vision | generative | [
"view synthesis",
"recurrent encoder-decoder",
"disentangling",
"3D rotation",
"weak supervision"
] | false | NeurIPS | 2,015 | main | true | null | false | 3411535f7888a943853895e8eef2bb0b6d328c2a | 317 | 11 | 0 | 499 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00710 | 2016-01-05 | 2,016 | Multi-Source Neural Translation | 2 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"multi-source machine translation",
"neural encoder-decoder",
"trilingual data",
"system combination",
"Bleu"
] | false | NAACL | 2,016 | main | true | null | false | f4610bbad14cf5c722cfe11fcca3d7e6382452dd | 338 | 30 | 1 | 320 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00720 | 2016-01-05 | 2,016 | How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites | 2 | q-bio.NC | [
"q-bio.NC",
"cs.AI"
] | true | true | theory | health | [
"sparse distributed representations",
"active dendrites",
"neocortex",
"NMDA spikes",
"pyramidal neurons"
] | true | null | null | null | false | null | false | ec3484e2ced345f2205aad5996664add2db4901c | 92 | 6 | 0 | 144 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00722 | 2016-01-05 | 2,016 | Matrix Variate RBM and Its Applications | 5 | cs.CV | [
"cs.CV"
] | true | true | generative | vision | [
"restricted boltzmann machine",
"matrix variate",
"generative model",
"image super-resolution",
"handwritten digit recognition"
] | false | null | null | null | false | null | false | 49c0de388eef92504f1591ec6e22f30fe243886b | 3 | 0 | 0 | 1,923 | false | 1 | [
{
"country": "CN",
"credit": 0.8
},
{
"country": "AU",
"credit": 0.2
}
] |
1601.00732 | 2016-01-06 | 2,016 | Low-Rank Representation over the Manifold of Curves | 4 | cs.CV | [
"cs.CV",
"cs.LG"
] | true | true | core_ml | null | [
"low-rank representation",
"functional data",
"manifold of curves",
"subspace clustering",
"square-root velocity function",
"shape analysis"
] | false | null | null | null | false | null | false | a2793331be3555933e30126e31fdaa4001bee228 | 2 | 0 | 0 | 245 | false | 1 | [
{
"country": "AU",
"credit": 0.75
},
{
"country": "US",
"credit": 0.25
}
] |
1601.00738 | 2016-01-05 | 2,016 | Resource Sharing for Multi-Tenant NoSQL Data Store in Cloud | 1 | cs.DC | [
"cs.DC",
"cs.AI",
"cs.DB",
"eess.SY"
] | true | true | systems | null | [] | false | null | null | null | false | null | false | e71a100ac9517e0bd37029f933c853e77d93145e | 2 | 0 | 0 | 347 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00740 | 2016-01-05 | 2,016 | Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture | 6 | cs.RO | [
"cs.RO",
"cs.CV",
"cs.LG"
] | true | true | robotics | time_series | [
"maneuver anticipation",
"sensory fusion",
"recurrent neural networks",
"LSTM",
"advanced driver assistance systems",
"driving context"
] | false | null | null | null | false | http://brain4cars.com | false | 2516a6065e96307d08d9a7ae4298160772c2faa0 | 150 | 26 | 0 | 185 | false | 1 | [
{
"country": "US",
"credit": 0.9999999999999999
}
] |
1601.00741 | 2016-01-05 | 2,016 | Learning Preferences for Manipulation Tasks from Online Coactive Feedback | 4 | cs.RO | [
"cs.RO",
"cs.AI",
"cs.LG"
] | true | true | robotics | rl | [
"coactive learning",
"preference learning",
"trajectory planning",
"manipulation",
"online learning",
"human feedback"
] | false | null | null | null | false | null | false | d1f037a0a46a7372fff7540d57d2d92d733151f1 | 132 | 19 | 0 | 185 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00770 | 2016-01-05 | 2,016 | End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures | 2 | cs.CL | [
"cs.CL",
"cs.LG"
] | true | true | llm_nlp | null | [
"relation extraction",
"entity recognition",
"LSTM",
"dependency tree",
"end-to-end learning",
"curriculum learning"
] | false | ACL | 2,016 | main | true | null | false | 3899f87a2031f3434f89beb68c11a1ca6428328a | 1,283 | 113 | 1 | 320 | false | 1 | [
{
"country": "JP",
"credit": 0.5
},
{
"country": "US",
"credit": 0.5
}
] |
1601.00781 | 2016-01-05 | 2,016 | Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features | 3 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"Computer Vision",
"Image Analysis",
"Multiple Object Detection",
"Object Localization",
"Pattern Matching"
] | true | null | null | null | false | null | false | b5ef499f3ea62468162b8f598e715be79f9a7c40 | 4 | 0 | 0 | 499 | false | 1 | [
{
"country": "PL",
"credit": 1
}
] |
1601.00816 | 2016-01-05 | 2,016 | Open challenges in understanding development and evolution of speech forms: The roles of embodied self-organization, motivation and active exploration | 1 | cs.AI | [
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] | true | true | speech_audio | null | [
"origins of speech forms",
"self-organization",
"curiosity",
"active exploration",
"development and evolution",
"evo-devo"
] | true | null | null | null | false | null | false | 94eb847ad8e2e4e8298370b4a2b9f82dd34d2ff3 | 6 | 0 | 0 | 291 | false | 1 | [
{
"country": "FR",
"credit": 1
}
] |
1601.00825 | 2016-01-05 | 2,016 | Gamifying Video Object Segmentation | 3 | cs.CV | [
"cs.CV"
] | true | true | vision | other | [
"interactive video object segmentation",
"games with a purpose",
"human in the loop",
"spatio-temporal superpixel segmentation"
] | true | null | null | null | false | null | false | bf677f58e05b09d349a38632d57a7b68e20fa57b | 13 | 1 | 0 | 499 | false | 1 | [
{
"country": "IT",
"credit": 1
}
] |
1601.00852 | 2016-01-05 | 2,016 | Self-learning and adaptation in a sensorimotor framework | 4 | cs.RO | [
"cs.RO"
] | true | true | robotics | rl | [
"sensorimotor learning",
"forward model",
"gaussian processes",
"automatic relevance determination",
"rrt*",
"adaptive control"
] | false | ICRA | 2,016 | main | true | null | false | 976a8240407508eb512b23619723b9eb128a8877 | 9 | 0 | 0 | 185 | false | 1 | [
{
"country": "SE",
"credit": 1
}
] |
1601.00863 | 2016-01-05 | 2,016 | Coordinate Friendly Structures, Algorithms and Applications | 5 | math.OC | [
"math.OC",
"cs.CE",
"cs.DC",
"math.NA",
"stat.ML"
] | true | true | optimization | systems | [
"coordinate update",
"fixed-point",
"operator splitting",
"parallel",
"asynchronous"
] | true | null | null | null | false | null | false | aae5a6c3330c6e5b31b7c2da725fd5e6138edb74 | 78 | 2 | 0 | 143 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00877 | 2016-01-20 | 2,016 | Proceedings of the Sixth International Workshop on Domain-Specific Languages and Models for Robotic Systems (DSLRob 2015) | 4 | cs.RO | [
"cs.RO"
] | false | null | robotics | other | null | null | null | null | null | false | null | null | 59e6d2624d9ddb515c2f95f16b170898cd19fa20 | 3 | 0 | 0 | 185 | null | null | null |
1601.00893 | 2016-01-05 | 2,016 | The Role of Context Types and Dimensionality in Learning Word Embeddings | 4 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"word embeddings",
"skip-gram",
"context types",
"dimensionality",
"intrinsic evaluation",
"extrinsic evaluation"
] | false | NAACL | 2,016 | main | true | http://code.google.com/p/word2vec/ | false | cf9830bf8d2babc3c32d192cdccb27aeaf46a048 | 127 | 9 | 0 | 320 | false | 1 | [
{
"country": "IL",
"credit": 0.25
},
{
"country": "US",
"credit": 0.75
}
] |
1601.00901 | 2016-01-05 | 2,016 | Joint learning of ontology and semantic parser from text | 2 | cs.AI | [
"cs.AI",
"cs.CL"
] | true | true | llm_nlp | graphs | [
"ontology learning",
"semantic parsing",
"grammar induction",
"context-free grammar"
] | true | null | null | null | false | null | false | f53933b4fb7b0f0dbf694910c016d671b85b8861 | 6 | 1 | 0 | 320 | false | 1 | [
{
"country": "SI",
"credit": 1
}
] |
1601.00909 | 2016-01-05 | 2,016 | The high-conductance state enables neural sampling in networks of LIF neurons | 5 | q-bio.NC | [
"q-bio.NC",
"cond-mat.dis-nn",
"cs.NE",
"physics.bio-ph",
"stat.ML"
] | true | false | theory | systems | [
"neural sampling",
"high-conductance state",
"leaky integrate-and-fire",
"neural response function",
"spiking neural networks"
] | false | null | null | null | false | null | false | 1b3cbff4c1f55175c5737e9641219ee5b677366f | 19 | 0 | 0 | 144 | false | 1 | [
{
"country": "DE",
"credit": 1
}
] |
1601.00917 | 2016-01-05 | 2,016 | DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks | 5 | cs.LG | [
"cs.LG",
"cs.NE"
] | true | true | systems | optimization | [
"hyperparameter optimization",
"reverse-mode automatic differentiation",
"deep neural networks",
"gradient-based tuning"
] | false | IJCAI | 2,016 | main | true | https://github.com/bigaidream-projects/drmad | true | c112084e0b3e18de82cd1e188b97c8a5997c3e65 | 32 | 6 | 0 | 347 | false | 0.8 | [
{
"country": "SG",
"credit": 0.6000000000000001
},
{
"country": "CN",
"credit": 0.2
}
] |
1601.00925 | 2016-01-05 | 2,016 | Complex Decomposition of the Negative Distance kernel | 3 | cs.LG | [
"cs.LG"
] | true | true | core_ml | null | [
"support vector machine",
"kernel function",
"text categorization",
"negative distance kernel",
"Mahalanobis distance"
] | true | null | null | null | false | null | false | 585a1b28028fdf2a7c32b6a0aa7eefbe00c571fe | 2 | 0 | 0 | 245 | false | 1 | [
{
"country": "CH",
"credit": 0.3333333333333333
},
{
"country": "DE",
"credit": 0.6666666666666666
}
] |
1601.00955 | 2016-01-05 | 2,016 | Optimally Pruning Decision Tree Ensembles With Feature Cost | 3 | stat.ML | [
"stat.ML",
"cs.LG"
] | true | true | optimization | null | [
"decision tree ensembles",
"ensemble pruning",
"feature cost",
"budgeted prediction",
"integer programming",
"linear programming relaxation"
] | false | null | null | null | false | null | false | 546536face9214ea528b3af897614f1f70f0f8c4 | 1 | 0 | 0 | 143 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00978 | 2016-01-05 | 2,016 | Crater Detection via Convolutional Neural Networks | 4 | cs.CV | [
"cs.CV"
] | true | true | vision | science | [
"crater detection",
"convolutional neural networks",
"satellite imagery",
"planetary science",
"feature learning"
] | false | null | null | null | false | null | false | 60588dc2c116825cb7d39005fa01c672e3a46a63 | 56 | 4 | 0 | 499 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.00998 | 2016-01-05 | 2,016 | Forecasting Social Navigation in Crowded Complex Scenes | 7 | cs.CV | [
"cs.CV",
"cs.RO",
"cs.SI"
] | true | true | robotics | time_series | [
"trajectory forecasting",
"social navigation",
"crowd scenes",
"multi-class interactions",
"social sensitivity",
"dataset"
] | false | null | null | null | false | null | false | cf0913d28e5805cbea3e91e456189531339a4992 | 28 | 1 | 0 | 185 | false | 1 | [
{
"country": "US",
"credit": 0.9999999999999998
}
] |
1601.01006 | 2016-01-05 | 2,016 | Space-Time Representation of People Based on 3D Skeletal Data: A Review | 4 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"skeleton-based representation",
"3D human pose",
"space-time features",
"action recognition",
"survey",
"RGB-D"
] | false | null | null | null | false | null | false | 7385f998caf17b2c6ae9f8808b6fd3a05205b438 | 320 | 9 | 0 | 499 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.01058 | 2016-01-08 | 2,016 | Wikiometrics: A Wikipedia Based Ranking System | 2 | cs.DL | [
"cs.DL",
"cs.AI",
"cs.SI"
] | true | true | other | null | [
"wikipedia",
"ranking",
"wikiometrics",
"metrics",
"wisdom of the crowd",
"university rankings"
] | false | null | null | null | false | null | false | 8d68bea492cde5e41fedb521eea87f2f98d48950 | 12 | 0 | 0 | 83 | false | 1 | [
{
"country": "IL",
"credit": 1
}
] |
1601.01060 | 2016-01-06 | 2,016 | Low-rank Matrix Factorization under General Mixture Noise Distributions | 5 | cs.CV | [
"cs.CV"
] | true | true | probabilistic | vision | [
"low-rank matrix factorization",
"mixture of exponential power",
"expectation maximization",
"markov random field",
"robust subspace learning",
"hyperspectral image restoration"
] | false | ICCV | 2,015 | main | true | null | false | 5964254f77f932ccd26e1c15801d14dd799cddf3 | 123 | 2 | 0 | 129 | false | 1 | [
{
"country": "CN",
"credit": 1
}
] |
1601.01073 | 2016-01-06 | 2,016 | Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism | 3 | cs.CL | [
"cs.CL",
"stat.ML"
] | true | true | llm_nlp | null | [
"neural machine translation",
"multilingual translation",
"attention mechanism",
"encoder-decoder",
"low-resource languages"
] | false | NAACL | 2,016 | main | true | null | false | 9ed9bff37ec952134564b3b2a022b7aba9479ff2 | 649 | 52 | 1 | 320 | false | 1 | [
{
"country": "TR",
"credit": 0.3333333333333333
},
{
"country": "US",
"credit": 0.3333333333333333
},
{
"country": "CA",
"credit": 0.3333333333333333
}
] |
1601.01085 | 2016-01-06 | 2,016 | Incorporating Structural Alignment Biases into an Attentional Neural Translation Model | 6 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"neural machine translation",
"attention",
"alignment models",
"structural biases",
"low resource translation"
] | false | NAACL | 2,016 | main | true | null | false | ada937c9f51316c6ac87f9d1d4509383d23e0c21 | 177 | 10 | 0 | 320 | false | 1 | [
{
"country": "AU",
"credit": 0.6666666666666666
},
{
"country": "US",
"credit": 0.3333333333333333
}
] |
1601.01100 | 2016-01-06 | 2,016 | Memory Matters: Convolutional Recurrent Neural Network for Scene Text Recognition | 4 | cs.CV | [
"cs.CV"
] | true | true | vision | null | [
"scene text recognition",
"convolutional recurrent neural network",
"LSTM",
"segmentation-free recognition",
"HMM",
"street view house numbers"
] | false | null | null | null | false | null | false | 42a0909be2b64660fa06557c9ccfad225687a7be | 3 | 0 | 0 | 499 | false | 1 | [
{
"country": "CN",
"credit": 1
}
] |
1601.01102 | 2016-01-06 | 2,016 | A Survey on Social Media Anomaly Detection | 5 | cs.LG | [
"cs.LG",
"cs.SI"
] | true | true | other | null | [
"anomaly detection",
"social media analysis",
"malicious activity detection",
"survey"
] | true | null | null | null | false | null | false | 2212e42e73bcc61e861428f32bf1180e81f3d049 | 127 | 2 | 1 | 83 | false | 1 | [
{
"country": "US",
"credit": 0.9646120112072116
},
{
"country": "FR",
"credit": 0.002679985381897917
},
{
"country": "CH",
"credit": 0.016713363381654283
},
{
"country": "CA",
"credit": 0.006931416737726886
},
{
"country": "IL",
"credit": 0.004897064197831648
... |
1601.01121 | 2016-01-06 | 2,016 | A pragmatic approach to multi-class classification | 4 | cs.LG | [
"cs.LG"
] | true | true | core_ml | null | [
"multi-class classification",
"hierarchical classification",
"decomposition approaches",
"cascade of classifiers",
"multilayer perceptrons",
"gesture recognition"
] | false | null | null | null | false | null | false | 8afe9ea1a3d922abe27b5efe7b1e87a1fc69ae5e | 18 | 0 | 0 | 245 | false | 1 | [
{
"country": "DE",
"credit": 0.75
},
{
"country": "FR",
"credit": 0.25
}
] |
1601.01142 | 2016-01-06 | 2,016 | Streaming Gibbs Sampling for LDA Model | 3 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | probabilistic | systems | [
"latent dirichlet allocation",
"gibbs sampling",
"streaming inference",
"online learning",
"topic models",
"distributed inference"
] | false | null | null | null | false | null | false | 5b1a911311759c656f977658038c5c666952d3a9 | 17 | 4 | 0 | 129 | false | 1 | [
{
"country": "US",
"credit": 0.3333333333333333
},
{
"country": "CN",
"credit": 0.6666666666666666
}
] |
1601.01145 | 2016-01-06 | 2,016 | Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study | 5 | cs.CV | [
"cs.CV"
] | true | false | vision | null | [
"vehicle classification",
"deep neural network",
"transfer learning",
"rear view images",
"vehicle detection",
"intelligent transportation"
] | true | null | null | null | false | null | false | c830ca44817a012bb2dea3cacedf7ae89d87af91 | 91 | 5 | 0 | 499 | false | 0.4 | [
{
"country": "SG",
"credit": 0.4
}
] |
1601.01157 | 2016-01-06 | 2,016 | A simple technique for improving multi-class classification with neural networks | 3 | cs.LG | [
"cs.LG"
] | true | true | core_ml | agents | [
"multi-class classification",
"one-against-all",
"neural networks",
"hand gesture recognition",
"score fusion",
"time-of-flight"
] | false | null | null | null | false | null | false | d402216f6bdcd361a884dea0301237925d54f8ec | 11 | 0 | 0 | 245 | false | 1 | [
{
"country": "DE",
"credit": 0.6666666666666666
},
{
"country": "FR",
"credit": 0.3333333333333333
}
] |
1601.01190 | 2016-01-06 | 2,016 | On Bayesian index policies for sequential resource allocation | 1 | stat.ML | [
"stat.ML"
] | true | true | probabilistic | rl | [
"multi-armed bandits",
"bayes-ucb",
"regret minimization",
"exponential families",
"index policies",
"kl-ucb"
] | false | null | null | null | false | null | false | 4e49ef0f20dbbf8aa940ecec7e26077d69359b60 | 87 | 11 | 0 | 129 | false | 1 | [
{
"country": "FR",
"credit": 1
}
] |
1601.01195 | 2016-01-06 | 2,016 | Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015 | 1 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"Part-of-Speech Tagging",
"Code Mixed",
"Social Media",
"HMM"
] | true | null | null | null | false | null | false | b99c93373d1ae0d105da67a67b87ab65d127b289 | 9 | 1 | 0 | 320 | false | 1 | [
{
"country": "IN",
"credit": 1
}
] |
1601.01216 | 2016-01-06 | 2,016 | Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis | 2 | cs.CV | [
"cs.CV"
] | true | true | science | health | [
"3D object detection",
"fluorescent microscopy",
"spatial statistics",
"colocalization analysis",
"protein foci",
"image segmentation"
] | true | null | null | null | false | null | false | 6eef3f8c3fdb16b726fbac18bf94a074cc992819 | 2 | 0 | 0 | 198 | false | 1 | [
{
"country": "DE",
"credit": 1
}
] |
1601.01218 | 2016-01-06 | 2,016 | Adaptive and Efficient Nonlinear Channel Equalization for Underwater Acoustic Communication | 3 | cs.LG | [
"cs.LG",
"cs.IT",
"cs.SD",
"math.IT"
] | true | true | other | speech_audio | [
"underwater acoustic communication",
"nonlinear channel equalization",
"piecewise linear models",
"adaptive algorithms",
"tree structure"
] | true | null | null | null | false | null | false | 72ff8cf992a6c85ec8f5929e4a981f494e465f44 | 13 | 1 | 0 | 83 | false | 1 | [
{
"country": "TR",
"credit": 0.6666666666666666
},
{
"country": "US",
"credit": 0.3333333333333333
}
] |
1601.01228 | 2016-01-06 | 2,016 | Some Experimental Issues in Financial Fraud Detection: An Investigation | 2 | cs.CR | [
"cs.CR",
"cs.AI"
] | true | true | other | null | [
"financial fraud detection",
"credit card fraud",
"performance metrics",
"computational intelligence",
"binary classification"
] | true | null | null | null | false | null | false | 05c4e9aff28aed057a7a8952d510f7529dd62d57 | 11 | 0 | 0 | 83 | false | 1 | [
{
"country": "AU",
"credit": 1
}
] |
1601.01232 | 2016-01-06 | 2,016 | Shape Animation with Combined Captured and Simulated Dynamics | 5 | cs.GR | [
"cs.GR",
"cs.CV"
] | true | true | robotics | graphs | [
"volumetric animation",
"performance capture",
"centroidal Voronoi tessellation",
"physical simulation",
"shape tracking"
] | false | null | null | null | false | null | false | c7ce491faa76097515ad19d3455bd87c747e384d | 1 | 0 | 0 | 185 | false | 1 | [
{
"country": "FR",
"credit": 1
}
] |
1601.01272 | 2016-01-06 | 2,016 | Recurrent Memory Networks for Language Modeling | 3 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"recurrent neural networks",
"language modeling",
"memory networks",
"LSTM",
"sentence completion",
"interpretability"
] | false | NAACL | 2,016 | main | true | null | false | 4a197ce36461849bcaee565b510a8ef71b7dcae3 | 21 | 1 | 0 | 320 | false | 1 | [
{
"country": "NL",
"credit": 1
}
] |
1601.01280 | 2016-01-06 | 2,016 | Language to Logical Form with Neural Attention | 2 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"semantic parsing",
"sequence-to-sequence",
"attention mechanism",
"logical forms",
"recurrent neural networks"
] | false | ACL | 2,016 | main | true | null | false | 558ac446dc26bee9789d660a251b75728cb6eeb2 | 800 | 97 | 1 | 320 | false | 1 | [
{
"country": "GB",
"credit": 1
}
] |
1601.01289 | 2016-01-06 | 2,016 | Internet of Drones | 3 | cs.NI | [
"cs.NI",
"cs.RO"
] | true | true | other | robotics | [
"unmanned aerial vehicles",
"drone traffic management",
"airspace management",
"layered network architecture",
"air traffic control",
"package delivery"
] | false | null | null | null | false | null | false | 183665c2bd41f68cb079b8a7c33b87529414ed48 | 167 | 6 | 1 | 83 | false | 1 | [
{
"country": "CA",
"credit": 1
}
] |
1601.01297 | 2016-01-07 | 2,016 | Angrier Birds: Bayesian reinforcement learning | 3 | cs.AI | [
"cs.AI",
"cs.LG"
] | true | true | rl | probabilistic | [
"Reinforcement Learning",
"Q-Learning",
"RLSVI",
"Exploration"
] | true | null | null | null | false | null | false | 4415e497eb72deb919ac286199697109c965ca56 | 1 | 0 | 0 | 412 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.01339 | 2016-01-06 | 2,016 | Quality Adaptive Low-Rank Based JPEG Decoding with Applications | 2 | cs.CV | [
"cs.CV"
] | true | true | core_ml | vision | [
"jpeg decoding",
"compression artifacts",
"image restoration",
"low-rank",
"deblurring",
"superresolution"
] | false | null | null | null | false | null | false | d7d9a998f61ad2a100c15051d33f66ca3b942b82 | 3 | 0 | 0 | 245 | false | 1 | [
{
"country": "CA",
"credit": 1
}
] |
1601.01343 | 2016-01-06 | 2,016 | Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation | 4 | cs.CL | [
"cs.CL"
] | true | true | llm_nlp | null | [
"named entity disambiguation",
"word embedding",
"entity embedding",
"skip-gram",
"knowledge base",
"Wikipedia"
] | false | null | null | null | false | null | false | 9a501e501a60b431b6031f81dc2c19b390b0aff3 | 339 | 64 | 1 | 320 | false | 1 | [
{
"country": "JP",
"credit": 1
}
] |
1601.01345 | 2016-01-06 | 2,016 | An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization | 2 | stat.ML | [
"stat.ML",
"math.ST"
] | true | true | theory | probabilistic | [
"quasi-bayesian aggregation",
"non-negative matrix factorization",
"oracle inequality",
"exponentially weighted aggregate",
"gibbs sampler",
"blockwise gradient descent"
] | false | null | null | null | false | null | false | 3c6bd2fb9a04d89b519595f9690bfe2238698ab1 | 17 | 0 | 0 | 144 | false | 1 | [
{
"country": "FR",
"credit": 1
}
] |
1601.01356 | 2016-01-07 | 2,016 | From Word Embeddings to Item Recommendation | 1 | cs.LG | [
"cs.LG",
"cs.CL",
"cs.IR",
"cs.SI"
] | true | true | retrieval | null | [
"recommendation systems",
"word embeddings",
"word2vec",
"skip-gram",
"location based social networks",
"check-ins"
] | true | null | null | null | false | null | false | 36afff98b0c752221e0ec49aac327f1eff05fe0c | 109 | 4 | 0 | 163 | false | 1 | [
{
"country": "TR",
"credit": 1
}
] |
1601.01411 | 2016-01-07 | 2,016 | Learning Kernels for Structured Prediction using Polynomial Kernel Transformations | 2 | cs.LG | [
"cs.LG",
"stat.ML"
] | true | true | core_ml | null | [
"kernel learning",
"structured prediction",
"schoenberg transform",
"hilbert-schmidt independence criterion",
"reproducing kernel hilbert space",
"polynomial kernels"
] | false | null | null | null | false | null | false | cd841235c0d4edab7941409336f42f7fbc052776 | 1 | 0 | 0 | 245 | false | 1 | [
{
"country": "US",
"credit": 1
}
] |
1601.01422 | 2016-01-07 | 2,016 | Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone | 4 | cs.CV | [
"cs.CV"
] | true | true | optimization | null | [
"metric learning",
"covariance descriptors",
"Dykstra algorithm",
"positive semi-definite cone",
"Bregman projection"
] | false | null | null | null | false | null | false | 7d9c8744b15b6ea5010303f290fcd1657c54d932 | 1 | 0 | 0 | 143 | false | 1 | [
{
"country": "JP",
"credit": 1
}
] |
1601.01431 | 2016-01-07 | 2,016 | Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis | 5 | cs.CV | [
"cs.CV"
] | true | true | core_ml | null | [
"probabilistic PCA",
"mixture models",
"bilateral projection",
"2D data",
"variational EM",
"dimensionality reduction"
] | false | CVPR | 2,016 | main | true | null | false | 52d4982ec40025d34f25fe62f45ccdcbf7e48fe8 | 4 | 0 | 0 | 245 | false | 1 | [
{
"country": "CN",
"credit": 0.8
},
{
"country": "AU",
"credit": 0.2
}
] |
1601.01432 | 2016-01-07 | 2,016 | Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition | 5 | cs.CV | [
"cs.CV"
] | true | true | core_ml | null | [
"sparse representation",
"dictionary learning",
"block-diagonal constraint",
"low-rank",
"recognition"
] | false | null | null | null | false | null | false | 106b54ed74f0fffaf6408a9b847d4ac0aa0ffef9 | 3 | 0 | 0 | 245 | false | 1 | [
{
"country": "CN",
"credit": 0.8
},
{
"country": "AU",
"credit": 0.2
}
] |
1601.01467 | 2016-01-07 | 2,016 | On Some Properties of Calibrated Trifocal Tensors | 1 | cs.CV | [
"cs.CV"
] | true | true | vision | theory | [
"calibrated trifocal tensor",
"trifocal essential matrix",
"multiview geometry",
"polynomial constraints",
"camera calibration"
] | false | null | null | null | false | null | false | fcd02a05afa817266c4371a0a7153dbedf0ca8e6 | 14 | 0 | 0 | 499 | false | 1 | [
{
"country": "RU",
"credit": 1
}
] |
1601.01492 | 2016-01-07 | 2,016 | Complexity of Shift Bribery in Committee Elections | 4 | cs.AI | [
"cs.AI",
"cs.GT"
] | true | true | theory | other | [
"shift bribery",
"multiwinner elections",
"computational complexity",
"committee elections",
"parameterized complexity",
"Chamberlin-Courant rule"
] | false | AAAI | 2,016 | main | true | null | false | 84b16c3904dcad5fe09cda4f31bb90bc7f67959b | 50 | 1 | 0 | 144 | false | 1 | [
{
"country": "DE",
"credit": 0.75
},
{
"country": "PL",
"credit": 0.25
}
] |
1601.01507 | 2016-01-07 | 2,016 | Fast Kronecker product kernel methods via generalized vec trick | 2 | stat.ML | [
"stat.ML",
"cs.LG"
] | true | false | core_ml | graphs | [
"kronecker product kernels",
"vec trick",
"zero-shot learning",
"kernel ridge regression",
"pair-input data",
"support vector machines"
] | false | null | null | null | false | null | false | 471fb2360d401801421e2074efe62530288b19d2 | 27 | 1 | 0 | 245 | false | 0 | [] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.