Merge pull request #182 from erodola/fix-links

removed all dois; added URL icons; added github icons

content/publication/10-1145-3414685-3417849/index.md

@@ -49,8 +49,9 @@ abstract: 'We introduce a novel computational framework for digital geometry pro
   our method through multiple applications in graphics, ranging from surface and signal
   denoising to enhancement, detail transfer, and cubic stylization.'
 publication: '*ACM Trans. Graph.*'
-doi: 10.1145/3414685.3417849
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://doi.org/10.1145/3414685.3417849
 ---

content/publication/10-1145-3519022/index.md

@@ -60,8 +60,9 @@ abstract: Deep learning approaches have recently raised the bar in many fields,
   in the current literature, discussing their pros and cons, and classifying them
   according to a rigorous taxonomy.
 publication: '*ACM Comput. Surv.*'
-doi: 10.1145/3519022
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://doi.org/10.1145/3519022
 ---

content/publication/3-dv-16-b/index.md

@@ -26,10 +26,10 @@ image:
   preview_only: false
 
 links:
-  - name: URL
-    url: https://profs.sci.univr.it/~castellani/allegati/3DV16_CR.pdf
-  - name: GitHub
-    url: 
+- icon: link
+  icon_pack: fas
+  name: 'URL'
+  url: https://profs.sci.univr.it/~castellani/allegati/3DV16_CR.pdf
 
 # Projects (optional).
 #   Associate this post with one or more of your projects.

content/publication/3-dv-19-b/index.md

@@ -40,10 +40,14 @@ abstract: 'We consider the problem of localizing relevant subsets of non-rigid g
 publication: "*Proc. Int'l Conference on 3D Vision (3DV)*"
 
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://www.computer.org/csdl/proceedings-article/3dv/2019/313100a037/1ezRALztN1m
 - name: PDF
   url: https://arxiv.org/pdf/1906.06226
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/AriannaRampini/HamiltonianSpectrumAlignment
 ---

content/publication/a-kernel-based-approach-for-irony-and-sarcasm-detection-in-italian/index.md

@@ -8,7 +8,6 @@ authors:
   - Roberto Basili
 author_notes:
   - (Oral Presentation + Best System Award Nomination)
-doi: https://doi.org/10.4000/books.aaccademia.4613
 publication: Proceedings of the 6th evaluation campaign of Natural Language
   Processing and Speech tools for Italia
 publication_short: EVALITA2018

content/publication/agresti-2020-experimental/index.md

@@ -54,7 +54,6 @@ abstract: 'The intrinsic random nature of quantum physics offers novel tools for
   we show that, for low levels of noise, our protocol offers an advantage over the
   simplest Bell-nonlocality protocol based on the Clauser-Horn-Shimony-Holt inequality.'
 publication: '*Communications Physics*'
-doi: https://doi.org/10.1038/s42005-020-0375-6
 links:
 - name: 'Communications Physics'
   url: https://www.nature.com/articles/s42005-020-0375-6

content/publication/arrigoni-2021-efficiently/index.md

@@ -29,7 +29,9 @@ image:
 links:
 - name: PDF
   url: https://arxiv.org/pdf/2110.13042.pdf
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/filthynobleman/AtA
 
 # Projects (optional).

content/publication/bloom-paper/index.md

@@ -5,7 +5,6 @@ publication_types:
 authors:
   - BIG-Science contributors including
   - santilli
-doi: 
 publication_short: arXiv
 abstract: "Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License."
 draft: false

content/publication/bonzi-2023-voice/index.md

@@ -40,6 +40,8 @@ publishDate: '2023-02-05T09:57:52.156096Z'
 abstract: "Singing voice detection (SVD) is an essential task in many music information retrieval (MIR) applications. Deep learning methods have shown promising results for SVD, but further performance improvements are desirable since it underlies many other tasks. This work proposes a novel SVD system combining a state-of-the-art music source separator (Demucs) with two downstream models: Long-term Recurrent Convolutional Network (LRCN) and a Transformer network. Our work highlights two main aspects: the impact of a music source separation model, such as Demucs, and its zero-shot capabilities for the SVD task; and the potential for deep learning to improve the system’s performance further. We evaluate our approach on three datasets (Jamendo Corpus, MedleyDB, and MIR-IK) and compare the performance of the two models to a baseline root mean square (RMS) algorithm and the current state-of-the-art for the Jamendo Corpus dataset."
 publication: '*International Workshop on Machine Learning for Signal Processing 2023*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://ieeexplore.ieee.org/document/10285863
 ---

content/publication/cannistraci-2023-ao/index.md

@@ -39,6 +39,8 @@ publication_types:
 abstract: 'The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications. Nevertheless, relative representations rely on a certain amount of parallel anchors to be given as input, which can be impractical to obtain in certain scenarios. To overcome this limitation, we propose an optimization-based method to discover new parallel anchors from a limited number of seeds. Our approach can be used to find semantic correspondence between different domains, align their relative spaces, and achieve competitive results in several tasks.'
 publication: 'Tiny Papers @ ICLR 2023'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url : https://openreview.net/pdf?id=VBuUL2IWlq
 ---

content/publication/cannistraci-2023-charts/index.md

@@ -39,7 +39,9 @@ publishDate: '2023-02-05T09:57:52.156096Z'
 abstract: "Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such latent spaces to create a unified space encompassing the combined information. To this end, we introduce Relative Latent Space Aggregation (RLSA), a two-step approach that first renders the spaces comparable using relative representations, and then aggregates them via a simple mean. We carefully divide a classification problem into a series of learning tasks under three different settings: sharing samples, classes, or neither. We then train a model on each task and aggregate the resulting latent spaces. We compare the aggregated space with that derived from an end-to-end model trained over all tasks and show that the two spaces are similar. We then observe that the aggregated space is better suited for classification, and empirically demonstrate that it is due to the unique imprints left by task-specific embedders within the representations. We finally test our framework in scenarios where no shared region exists and show that it can still be used to merge the spaces, albeit with diminished benefits over naive merging."
 publication: '*NeurReps Workshop 2023*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://openreview.net/forum?id=ZFu7CPtznY
 - name: PDF
   url: https://openreview.net/pdf?id=ZFu7CPtznY

content/publication/cannistraci-2023-infusing/index.md

@@ -38,7 +38,9 @@ publication_types:
 abstract: 'It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases. From a geometric perspective, identifying the classes of transformations and the related invariances that connect these representations is fundamental to unlocking applications, such as merging, stitching, and reusing different neural modules. However, estimating task-specific transformations a priori can be challenging and expensive due to several factors (e.g., weights initialization, training hyperparameters, or data modality). To this end, we introduce a versatile method to directly incorporate a set of invariances into the representations, constructing a product space of invariant components on top of the latent representations without requiring prior knowledge about the optimal invariance to infuse. We validate our solution on classification and reconstruction tasks, observing consistent latent similarity and downstream performance improvements in a zero-shot stitching setting. The experimental analysis comprises three modalities (vision, text, and graphs), twelve pretrained foundational models, eight benchmarks, and several architectures trained from scratch.'
 publication: '*International Conference on Learning Representations (ICLR 2024)*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url : https://openreview.net/forum?id=vngVydDWft
 - name: PDF
   url: https://openreview.net/pdf?id=vngVydDWft

content/publication/cgf-17-c/index.md

@@ -26,10 +26,14 @@ image:
   preview_only: false
 
 links:
-  - name: URL
-    url: https://arxiv.org/pdf/1707.02596.pdf
-  - name: GitHub
-    url: http://vision.in.tum.de/_media/spezial/bib/lmh-code.zip
+- icon: link
+  icon_pack: fas
+  name: 'URL'
+  url: https://arxiv.org/pdf/1707.02596.pdf
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
+  url: http://vision.in.tum.de/_media/spezial/bib/lmh-code.zip
 
 # Projects (optional).
 #   Associate this post with one or more of your projects.

content/publication/cgf-18/index.md

@@ -28,10 +28,14 @@ image:
   preview_only: false
 
 links:
-  - name: URL
-    url: http://www.lix.polytechnique.fr/~maks/papers/product_transfer.pdf
-  - name: GitHub
-    url: https://drive.google.com/file/d/1QSHeZYJC58blbgV9mj0SSlZMDRiGIlKU/view
+- icon: link
+  icon_pack: fas
+  name: 'URL'
+  url: http://www.lix.polytechnique.fr/~maks/papers/product_transfer.pdf
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
+  url: https://drive.google.com/file/d/1QSHeZYJC58blbgV9mj0SSlZMDRiGIlKU/view
 
 # Projects (optional).
 #   Associate this post with one or more of your projects.

content/publication/colombo-22/index.md

@@ -26,10 +26,14 @@ image:
   preview_only: false
 
 links:
-  - name: URL
-    url: https://diglib.eg.org/bitstream/handle/10.2312/stag20221250/001-009.pdf
-  - name: GitHub
-    url: https://github.com/michele-colombo/PC-Gau_STAG2022
+- icon: link
+  icon_pack: fas
+  name: 'URL'
+  url: https://diglib.eg.org/bitstream/handle/10.2312/stag20221250/001-009.pdf
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
+  url: https://github.com/michele-colombo/PC-Gau_STAG2022
 
 # Projects (optional).
 #   Associate this post with one or more of your projects.
@@ -59,5 +63,4 @@ abstract: Shape matching is a central problem in geometry processing application
   to LB - when employed in the same shape-matching pipeline.
 publication: '*Smart Tools and Applications in Graphics - Eurographics Italian Chapter
   Conference*'
-doi: 10.2312/stag.20221253
 ---

content/publication/cosmo-20161/index.md

@@ -45,7 +45,6 @@ abstract: 'We consider the problem of deformable object detection and dense corr
   accurate results in challenging settings that were previously left unexplored in
   the literature.'
 publication: '*Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016*'
-doi: 10.1109/3DV.2016.10
 links:
 - name: PDF
   url: https://www.dais.unive.it/~cosmo/content/SCOPUS_ID:85011309727.pdf

content/publication/cosmo-201661/index.md

@@ -47,10 +47,11 @@ abstract: 'Matching deformable 3D shapes under partiality transformations is a c
   presents the details of the dataset, the adopted evaluation measures, and shows
   thorough comparisons among all competing methods.'
 publication: '*Eurographics Workshop on 3D Object Retrieval, EG 3DOR*'
-doi: 10.2312/3dor.20161089
 links:
 - name: PDF
   url: https://diglib.eg.org/bitstream/handle/10.2312/3dor20161089/061-067.pdf
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://www.dais.unive.it/~shrec2016
 ---

content/publication/cosmo-2017209/index.md

@@ -51,7 +51,6 @@ abstract: Recent efforts in the area of joint object matching approach the probl
   optimization procedure which assures accurate and provably consistent solutions
   in a matter of minutes in collections with hundreds of shapes.
 publication: '*Computer Graphics Forum*'
-doi: 10.1111/cgf.12796
 links:
 - name: PDF
   url: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cgf.12796

content/publication/cosmo-20197521/index.md

@@ -51,10 +51,11 @@ abstract: The question whether one can recover the shape of a geometric object f
   dense deformable correspondence.
 publication: '*Proceedings of the IEEE Computer Society Conference on Computer Vision
   and Pattern Recognition*'
-doi: 10.1109/CVPR.2019.00771
 links:
 - name: PDF
   url: https://openaccess.thecvf.com/content_CVPR_2019/papers/Cosmo_Isospectralization_or_How_to_Hear_Shape_Style_and_Correspondence_CVPR_2019_paper.pdf
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/lcosmo/isospectralization
 ---

content/publication/cosmo-2020-limp/index.md

@@ -54,6 +54,8 @@ publication: '*Computer Vision--ECCV 2020*'
 links:
 - name: 'arXiv'
   url: https://arxiv.org/abs/2003.12283
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/lcosmo/LIMP
 ---

content/publication/cosmo-20201/index.md

@@ -50,10 +50,11 @@ abstract: We introduce the Average Mixing Kernel Signature (AMKS), a novel signa
   Kernel Signature (WKS).
 publication: '*Lecture Notes in Computer Science (including subseries Lecture Notes
   in Artificial Intelligence and Lecture Notes in Bioinformatics)*'
-doi: 10.1007/978-3-030-58565-5_1
 links:
 - name: 'PDF'
   url: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650001.pdf
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/lcosmo/amks-descriptor
 ---

content/publication/cosmo-20221474/index.md

@@ -53,11 +53,12 @@ abstract: The Average Mixing Kernel Signature is a novel spectral signature for
   suggest that the AMKS should be the signature of choice for various compute vision
   problems, including as input of deep convolutional architectures for shape analysis.
 publication: '*International Journal of Computer Vision*'
-doi: 10.1007/s11263-022-01610-y
 links:
 - name: PDF
   url: https://link.springer.com/article/10.1007/s11263-022-01610-y
-- name: GitHub
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
   url: https://github.com/lcosmo/amks-descriptor
 #- name: 'arXiv'
 #  url: https://arxiv.org/abs/2301.08562

content/publication/cosmo-2024-kernel/index.md

@@ -41,7 +41,9 @@ publication_types:
 abstract: "The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this article, we propose to use graph kernels, i.e., kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability in terms of the structural masks that are learned during the training process, similar to what happens for convolutional masks in traditional convolutional neural networks (CNNs). We perform an extensive ablation study to investigate the model hyperparameters’ impact and show that our model achieves competitive performance on standard graph classification and regression datasets."
 publication: '*IEEE Transactions on Neural Networks and Learning Systems*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://ieeexplore.ieee.org/document/10542111
 - name: arXiv
   url: https://arxiv.org/abs/2112.07436

content/publication/crisostomi-2023-avengr/index.md

@@ -35,6 +35,8 @@ publication_types:
 abstract: Getting a good understanding of the user intent is vital for e-commerce applications to surface the right product to a given customer query. Query Understanding (QU) systems are essential for this purpose, and many e-commerce providers are working on complex solutions that need to be data efficient and able to capture early emerging market trends. Query Attribute Understanding (QAU) is a sub-component of QU that involves extracting named attributes from user queries and linking them to existing e-commerce entities such as brand, material, color, etc. While extracting named entities from text has been extensively explored in the literature, QAU requires specific attention due to the nature of the queries, which are often short, noisy, ambiguous, and constantly evolving. This paper makes three contributions to QAU. First, we propose a novel end-to-end approach that jointly solves Named Entity Recognition (NER) and Entity Linking (NEL) and enables open-world reasoning for QAU. Second, we introduce a novel method for utilizing product graphs to enhance the representation of query entities. Finally, we present a new dataset constructed from public sources that can be used to evaluate the performance of future QAU systems.
 publication: '*Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://aclanthology.org/2023.acl-industry.14/
 ---

content/publication/crisostomi-2023-ub/index.md

@@ -40,6 +40,8 @@ publication_types:
 abstract: Datasets used to train deep learning models in industrial settings often exhibit skewed distributions with some samples repeated a large number of times.This paper presents a simple yet effective solution to reduce the increased burden of repeated computation on redundant datasets.Our approach eliminates duplicates at the batch level, without altering the data distribution observed by the model, making it model-agnostic and easy to implement as a plug-and-play module. We also provide a mathematical expression to estimate the reduction in training time that our approach provides. Through empirical evidence, we show that our approach significantly reduces training times on various models across datasets with varying redundancy factors, without impacting their performance on the Named Entity Recognition task, both on publicly available datasets and in real industrial settings.In the latter, the approach speeds training by up to 87{\%}, and by 46{\%} on average, with a drop in model performance of 0.2{\%} relative at worst.We finally release a modular and reusable codebase to further advance research in this area..
 publication: '*Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://aclanthology.org/2023.acl-industry.23/
 ---

content/publication/crisostomi-etal-2022-play/index.md

@@ -59,6 +59,8 @@ abstract: Cross-lingual phenomena are quite common in informal contexts like soc
 publication: '*Proceedings of the Massively Multilingual Natural Language Understanding
   Workshop (MMNLU-22)*'
 links:
-- name: URL
+- icon: link
+  icon_pack: fas
+  name: 'URL'
   url: https://aclanthology.org/2022.mmnlu-1.5
 ---

content/publication/cvpr-19-a/index.md

@@ -17,7 +17,7 @@ date: '2019-06-01'
 lastmod: 2023-02-02T06:55:07+01:00
 featured: false
 draft: false
-publication_short: ""
+publication_short: "CVPR 2019"
 
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder.
@@ -28,10 +28,16 @@ image:
   preview_only: false
 
 links:
-  - name: URL
-    url: https://drive.google.com/file/d/1ewpPCCkYMBpCsJ2FAbULzMbsFE0VOuaq/view
-  - name: GitHub
-    url: https://drive.google.com/file/d/17Th7QFhX2dOobcZM38AIqPaBQYg7nldW/view
+- icon: link
+  icon_pack: fas
+  name: 'URL'
+  url: https://ieeexplore.ieee.org/document/8953995
+- name: PDF
+  url: https://openaccess.thecvf.com/content_CVPR_2019/papers/Melzi_GFrames_Gradient-Based_Local_Reference_Frame_for_3D_Shape_Matching_CVPR_2019_paper.pdf
+- icon: github
+  icon_pack: fab
+  name: 'GitHub'
+  url: https://drive.google.com/file/d/17Th7QFhX2dOobcZM38AIqPaBQYg7nldW/view
 
 # Projects (optional).
 #   Associate this post with one or more of your projects.